diff --git a/pytorch_forecasting/models/duet/dataset/AQWan.csv b/pytorch_forecasting/models/duet/dataset/AQWan.csv
new file mode 100644
index 000000000..aa6c86075
--- /dev/null
+++ b/pytorch_forecasting/models/duet/dataset/AQWan.csv
@@ -0,0 +1,385705 @@
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diff --git a/pytorch_forecasting/models/duet/duet.py b/pytorch_forecasting/models/duet/duet.py
new file mode 100644
index 000000000..4da410829
--- /dev/null
+++ b/pytorch_forecasting/models/duet/duet.py
@@ -0,0 +1,313 @@
+from copy import copy
+from logging import config
+from typing import Callable, Optional, Union
+
+import numpy as np
+import torch
+import torch.nn as nn
+
+from pytorch_forecasting.utils._dependencies._safe_import import _safe_import
+
+Metric = _safe_import("torchmetrics.Metric")
+rearrange = _safe_import("einops.rearrange")
+
+from pytorch_forecasting.data import TimeSeriesDataSet
+from pytorch_forecasting.metrics import (
+    MAE,
+    MAPE,
+    RMSE,
+    SMAPE,
+    MultiHorizonMetric,
+    QuantileLoss,
+)
+from pytorch_forecasting.models.base import BaseModelWithCovariates
+from pytorch_forecasting.models.duet.sub_modules.layers.linear_extractor_cluster import (  # noqa: E501
+    Linear_extractor_cluster,
+)
+from pytorch_forecasting.models.duet.sub_modules.utils.masked_attention import (
+    AttentionLayer,
+    Encoder,
+    EncoderLayer,
+    FullAttention,
+    Mahalanobis_mask,
+)
+from pytorch_forecasting.models.nn.embeddings import MultiEmbedding
+
+# Default hyperparameters as specified in the official implementation.
+DEFAULT_DUET_HYPER_PARAMS = {
+    "enc_in": 1,
+    "dec_in": 1,
+    "c_out": 1,
+    "e_layers": 2,
+    "d_layers": 1,
+    "d_model": 512,
+    "d_ff": 2048,
+    "hidden_size": 256,
+    "freq": "h",
+    "factor": 1,
+    "n_heads": 8,
+    "activation": "gelu",
+    "output_attention": 0,
+    "patch_len": 16,
+    "stride": 8,
+    "dropout": 0.2,
+    "fc_dropout": 0.2,
+    "moving_avg": 25,
+    "batch_size": 256,
+    "lradj": "type3",
+    "num_workers": 0,
+    "patience": 10,
+    "num_experts": 4,
+    "noisy_gating": True,
+    "k": 1,
+    "CI": False,
+}
+
+
+class DUETModel(BaseModelWithCovariates):
+    """
+    Initial implementation of DUET: Dual Clustering Enhanced Multivariate Time
+    Series Forecasting
+
+    Original paper: https://arxiv.org/pdf/2412.10859
+    """
+
+    def __init__(
+        self,
+        # Parameters for BaseModelWithCovariates
+        static_categoricals: list[str],
+        static_reals: list[str],
+        time_varying_categoricals_encoder: list[str],
+        time_varying_categoricals_decoder: list[str],
+        time_varying_reals_encoder: list[str],
+        time_varying_reals_decoder: list[str],
+        x_reals: list[str],
+        x_categoricals: list[str],
+        embedding_sizes: dict,
+        embedding_labels: dict,
+        embedding_paddings: list[str],
+        categorical_groups: dict,
+        # Parameters for BaseModel
+        loss: Metric,
+        learning_rate: float,
+        dataset_parameters: dict,
+        optimizer: str = "adam",
+        output_transformer: Callable = None,
+        # Parameters for DUET's architecture
+        dec_in: int = 1,
+        c_out: int = 1,
+        e_layers: int = 2,
+        d_layers: int = 1,
+        d_model: int = 512,
+        d_ff: int = 2048,
+        hidden_size: int = 256,
+        freq: str = "h",
+        factor: int = 1,
+        n_heads: int = 8,
+        activation: str = "gelu",
+        output_attention: int = 0,
+        patch_len: int = 16,
+        stride: int = 8,
+        dropout: float = 0.2,
+        fc_dropout: float = 0.2,
+        moving_avg: int = 25,
+        lradj: str = "type3",
+        num_workers: int = 0,
+        patience: int = 10,
+        num_experts: int = 4,
+        noisy_gating: bool = True,
+        k: int = 1,
+        CI: bool = False,
+        **kwargs,
+    ):
+        """
+        Initialize DUET model.
+
+        Args:
+            static_categoricals: names of static categorical variables
+            static_reals: names of static continuous variables
+            time_varying_categoricals_encoder: names of categorical variables for encoder
+            time_varying_categoricals_decoder: names of categorical variables for decoder
+            time_varying_reals_encoder: names of continuous variables for encoder
+            time_varying_reals_decoder: names of continuous variables for decoder
+            x_reals: order of continuous variables in tensor passed to forward function
+            x_categoricals: order of categorical variables in tensor passed to forward function
+            embedding_sizes: dictionary mapping (string) indices to tuple of number of categorical classes and
+                embedding size
+            embedding_paddings: list of indices for embeddings which transform the zero's embedding to a zero vector
+            embedding_labels: dictionary mapping (string) indices to list of categorical labels
+            categorical_groups: dictionary where values
+                are list of categorical variables that are forming together a new categorical
+                variable which is the key in the dictionary
+            loss: loss function to use. Can be any instance of torchmetrics.Metric
+            learning_rate: learning rate to use
+            dataset_parameters: dictionary containing parameters of the dataset
+            optimizer: optimizer to use (default: "adam")
+            output_transformer: function to transform the output of the network
+            dec_in: number of input features to the decoder
+            c_out: number of output features
+            e_layers: number of encoder layers
+            d_layers: number of decoder layers
+            d_model: dimensionality of the model's hidden states. It is the size of the
+                vectors used to represent the time series data throughout the model.
+            d_ff: dimensionality of the feedforward network model
+            hidden_size: hidden size for the distributional router's encoder,
+                which is part of the Mixture of Experts mechanism.
+            freq: frequency of the time series data. This is used for generating time
+                based features.
+            factor: factor for the attention mechanism
+            n_heads: number of attention heads
+            activation: activation function used in the model
+            output_attention: whether to output attention weights
+            patch_len: length of each patch when using patching mechanism
+            stride: stride for the patching mechanism
+            dropout: dropout rate applied within the encoder layers to prevent overfitting
+            fc_dropout: dropout rate applied to the final fully connected layer
+            moving_avg: window size for moving average, used in the decomposition of the time series
+            batch_size: batch size used during training
+            lradj: learning rate adjustment strategy
+            num_workers: number of workers for data loading
+            patience: number of epochs with no improvement after which training will be stopped
+            num_experts: number of experts in the Mixture of Experts mechanism
+            noisy_gating: whether to use noisy gating in the Mixture of Experts mechanism
+            k: number of experts to be selected by the router for each input
+            CI: whether to use channel independent configuration. If True, the model
+                processes each channel (variate) of the time series independently before combining them.
+            **kwargs: additional arguments
+        """  # noqa: E501
+        self.save_hyperparameters()
+        super().__init__(
+            loss=loss,
+            learning_rate=learning_rate,
+            optimizer=optimizer,
+            output_transformer=output_transformer,
+            dataset_parameters=dataset_parameters,
+        )
+
+        self.cat_embeddings = MultiEmbedding(
+            embedding_sizes=self.hparams.embedding_sizes,
+            embedding_paddings=self.hparams.embedding_paddings,
+            categorical_groups=self.hparams.categorical_groups,
+            x_categoricals=self.hparams.x_categoricals,
+        )
+
+        self.cluster = Linear_extractor_cluster(self.hparams)
+        self.CI = self.hparams.CI
+        self.n_vars = self.hparams.enc_in
+        self.mask_generator = Mahalanobis_mask(self.hparams.seq_len)
+        self.Channel_transformer = Encoder(
+            [
+                EncoderLayer(
+                    AttentionLayer(
+                        FullAttention(
+                            True,
+                            self.hparams.factor,
+                            attention_dropout=self.hparams.dropout,
+                            output_attention=self.hparams.output_attention,
+                        ),
+                        self.hparams.d_model,
+                        self.hparams.n_heads,
+                    ),
+                    self.hparams.d_model,
+                    self.hparams.d_ff,
+                    dropout=self.hparams.dropout,
+                    activation=self.hparams.activation,
+                )
+                for _ in range(self.hparams.e_layers)
+            ],
+            norm_layer=torch.nn.LayerNorm(self.hparams.d_model),
+        )
+
+        self.linear_head = nn.Sequential(
+            nn.Linear(self.hparams.d_model, self.hparams.pred_len),
+            nn.Dropout(self.hparams.fc_dropout),
+        )
+
+    def forward(self, x: dict) -> dict:
+        embedded_features = self.cat_embeddings(x["encoder_cat"])
+        cont_tensor = x["encoder_cont"]
+
+        input_tensor = torch.cat([cont_tensor, embedded_features["cols"]], dim=-1)
+
+        batch_size = input_tensor.shape[0]  # noqa: F841
+
+        if self.hparams.CI:
+            channel_independent_input = rearrange(input_tensor, "b l n -> (b n) l 1")
+
+            reshaped_output, L_importance = self.cluster(channel_independent_input)
+
+            temporal_feature = rearrange(
+                reshaped_output, "(b n) l 1 -> b l n", b=input_tensor.shape[0]
+            )  # noqa: E501
+        else:
+            temporal_feature, L_importance = self.cluster(input_tensor)
+
+        temporal_feature = rearrange(temporal_feature, "b d n -> b n d")
+
+        if self.n_vars > 1:
+            # Multivariate case: apply channel transformer
+
+            changed_input = rearrange(input_tensor, "b l n -> b n l")
+
+            channel_mask = self.mask_generator(changed_input)
+
+            channel_group_feature, _ = self.Channel_transformer(
+                x=temporal_feature, attn_mask=channel_mask
+            )
+        else:
+            # For univariate case, the group feature is just the temporal feature
+            channel_group_feature = temporal_feature
+
+        # Select the features for the target variable(s)
+        target_features = torch.stack(
+            [
+                channel_group_feature[i, self.target_positions]
+                for i in range(channel_group_feature.size(0))
+            ],
+            dim=0,
+        )
+
+        # if torch.isnan(target_features).any():
+        #     raise ValueError("Target features contain NaN values.")
+
+        # Passing only the target features to the prediction head
+        normalized_prediction = self.linear_head(target_features)
+
+        # if torch.isnan(normalized_prediction).any():
+        #     raise ValueError("Predictions contain NaN values.")
+
+        normalized_prediction = rearrange(normalized_prediction, "b n d -> b d n")
+
+        prediction = self.transform_output(
+            prediction=normalized_prediction, target_scale=x["target_scale"]
+        )
+
+        return self.to_network_output(prediction=prediction, L_importance=L_importance)
+
+    @classmethod
+    def from_dataset(
+        cls,
+        dataset: TimeSeriesDataSet,
+        allowed_encoder_known_variable_names: list[str] = None,
+        **kwargs,
+    ):
+        """
+        Create a DUET model from a TimeSeriesDataSet.
+        """
+        new_kwargs = DEFAULT_DUET_HYPER_PARAMS.copy()
+        new_kwargs.update(kwargs)
+
+        # Adding parameters we infer from the dataset
+        new_kwargs.update(
+            {
+                "seq_len": dataset.max_encoder_length,
+                "pred_len": dataset.max_prediction_length,
+                "enc_in": len(dataset.reals) + len(dataset.categoricals),
+            }
+        )
+
+        return super().from_dataset(
+            dataset,
+            allowed_encoder_known_variable_names=allowed_encoder_known_variable_names,
+            **new_kwargs,
+        )
diff --git a/pytorch_forecasting/models/duet/sub_modules/layers/Autoformer_EncDec.py b/pytorch_forecasting/models/duet/sub_modules/layers/Autoformer_EncDec.py
new file mode 100644
index 000000000..85db66dfc
--- /dev/null
+++ b/pytorch_forecasting/models/duet/sub_modules/layers/Autoformer_EncDec.py
@@ -0,0 +1,232 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class my_Layernorm(nn.Module):
+    """
+    Special designed layernorm for the seasonal part
+    """
+
+    def __init__(self, channels):
+        super().__init__()
+        self.layernorm = nn.LayerNorm(channels)
+
+    def forward(self, x):
+        x_hat = self.layernorm(x)
+        bias = torch.mean(x_hat, dim=1).unsqueeze(1).repeat(1, x.shape[1], 1)
+        return x_hat - bias
+
+
+class moving_avg(nn.Module):
+    """
+    Moving average block to highlight the trend of time data
+    """
+
+    def __init__(self, kernel_size, stride):
+        super().__init__()
+        self.kernel_size = kernel_size
+        self.avg = nn.AvgPool1d(kernel_size=kernel_size, stride=stride, padding=0)
+
+    def forward(self, x):
+        # padding on the both ends of time data
+        front = x[:, 0:1, :].repeat(1, (self.kernel_size - 1) // 2, 1)
+        end = x[:, -1:, :].repeat(1, (self.kernel_size - 1) // 2, 1)
+        x = torch.cat([front, x, end], dim=1)
+        x = self.avg(x.permute(0, 2, 1))
+        x = x.permute(0, 2, 1)
+        return x
+
+
+class series_decomp(nn.Module):
+    """
+    Series decomposition block
+    """
+
+    def __init__(self, kernel_size):
+        super().__init__()
+        self.moving_avg = moving_avg(kernel_size, stride=1)
+
+    def forward(self, x):
+        moving_mean = self.moving_avg(x)
+        res = x - moving_mean
+        return res, moving_mean
+
+
+class series_decomp_multi(nn.Module):
+    """
+    Multiple Series decomposition block from FEDformer
+    """
+
+    def __init__(self, kernel_size):
+        super().__init__()
+        self.kernel_size = kernel_size
+        self.series_decomp = [series_decomp(kernel) for kernel in kernel_size]
+
+    def forward(self, x):
+        moving_mean = []
+        res = []
+        for func in self.series_decomp:
+            sea, moving_avg = func(x)
+            moving_mean.append(moving_avg)
+            res.append(sea)
+
+        sea = sum(res) / len(res)
+        moving_mean = sum(moving_mean) / len(moving_mean)
+        return sea, moving_mean
+
+
+class EncoderLayer(nn.Module):
+    """
+    Autoformer encoder layer with the progressive decomposition architecture
+    """
+
+    def __init__(
+        self,
+        attention,
+        d_model,
+        d_ff=None,
+        moving_avg=25,
+        dropout=0.1,
+        activation="relu",
+    ):
+        super().__init__()
+        d_ff = d_ff or 4 * d_model
+        self.attention = attention
+        self.conv1 = nn.Conv1d(
+            in_channels=d_model, out_channels=d_ff, kernel_size=1, bias=False
+        )
+        self.conv2 = nn.Conv1d(
+            in_channels=d_ff, out_channels=d_model, kernel_size=1, bias=False
+        )
+        self.decomp1 = series_decomp(moving_avg)
+        self.decomp2 = series_decomp(moving_avg)
+        self.dropout = nn.Dropout(dropout)
+        self.activation = F.relu if activation == "relu" else F.gelu
+
+    def forward(self, x, attn_mask=None):
+        new_x, attn = self.attention(x, x, x, attn_mask=attn_mask)
+        x = x + self.dropout(new_x)
+        x, _ = self.decomp1(x)
+        y = x
+        y = self.dropout(self.activation(self.conv1(y.transpose(-1, 1))))
+        y = self.dropout(self.conv2(y).transpose(-1, 1))
+        res, _ = self.decomp2(x + y)
+        return res, attn
+
+
+class Encoder(nn.Module):
+    """
+    Autoformer encoder
+    """
+
+    def __init__(self, attn_layers, conv_layers=None, norm_layer=None):
+        super().__init__()
+        self.attn_layers = nn.ModuleList(attn_layers)
+        self.conv_layers = (
+            nn.ModuleList(conv_layers) if conv_layers is not None else None
+        )
+        self.norm = norm_layer
+
+    def forward(self, x, attn_mask=None):
+        attns = []
+        if self.conv_layers is not None:
+            for attn_layer, conv_layer in zip(self.attn_layers, self.conv_layers):
+                x, attn = attn_layer(x, attn_mask=attn_mask)
+                x = conv_layer(x)
+                attns.append(attn)
+            x, attn = self.attn_layers[-1](x)
+            attns.append(attn)
+        else:
+            for attn_layer in self.attn_layers:
+                x, attn = attn_layer(x, attn_mask=attn_mask)
+                attns.append(attn)
+
+        if self.norm is not None:
+            x = self.norm(x)
+
+        return x, attns
+
+
+class DecoderLayer(nn.Module):
+    """
+    Autoformer decoder layer with the progressive decomposition architecture
+    """
+
+    def __init__(
+        self,
+        self_attention,
+        cross_attention,
+        d_model,
+        c_out,
+        d_ff=None,
+        moving_avg=25,
+        dropout=0.1,
+        activation="relu",
+    ):
+        super().__init__()
+        d_ff = d_ff or 4 * d_model
+        self.self_attention = self_attention
+        self.cross_attention = cross_attention
+        self.conv1 = nn.Conv1d(
+            in_channels=d_model, out_channels=d_ff, kernel_size=1, bias=False
+        )
+        self.conv2 = nn.Conv1d(
+            in_channels=d_ff, out_channels=d_model, kernel_size=1, bias=False
+        )
+        self.decomp1 = series_decomp(moving_avg)
+        self.decomp2 = series_decomp(moving_avg)
+        self.decomp3 = series_decomp(moving_avg)
+        self.dropout = nn.Dropout(dropout)
+        self.projection = nn.Conv1d(
+            in_channels=d_model,
+            out_channels=c_out,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            padding_mode="circular",
+            bias=False,
+        )
+        self.activation = F.relu if activation == "relu" else F.gelu
+
+    def forward(self, x, cross, x_mask=None, cross_mask=None):
+        x = x + self.dropout(self.self_attention(x, x, x, attn_mask=x_mask)[0])
+        x, trend1 = self.decomp1(x)
+        x = x + self.dropout(
+            self.cross_attention(x, cross, cross, attn_mask=cross_mask)[0]
+        )
+        x, trend2 = self.decomp2(x)
+        y = x
+        y = self.dropout(self.activation(self.conv1(y.transpose(-1, 1))))
+        y = self.dropout(self.conv2(y).transpose(-1, 1))
+        x, trend3 = self.decomp3(x + y)
+
+        residual_trend = trend1 + trend2 + trend3
+        residual_trend = self.projection(residual_trend.permute(0, 2, 1)).transpose(
+            1, 2
+        )
+        return x, residual_trend
+
+
+class Decoder(nn.Module):
+    """
+    Autoformer encoder
+    """
+
+    def __init__(self, layers, norm_layer=None, projection=None):
+        super().__init__()
+        self.layers = nn.ModuleList(layers)
+        self.norm = norm_layer
+        self.projection = projection
+
+    def forward(self, x, cross, x_mask=None, cross_mask=None, trend=None):
+        for layer in self.layers:
+            x, residual_trend = layer(x, cross, x_mask=x_mask, cross_mask=cross_mask)
+            trend = trend + residual_trend
+
+        if self.norm is not None:
+            x = self.norm(x)
+
+        if self.projection is not None:
+            x = self.projection(x)
+        return x, trend
diff --git a/pytorch_forecasting/models/duet/sub_modules/layers/Embed.py b/pytorch_forecasting/models/duet/sub_modules/layers/Embed.py
new file mode 100644
index 000000000..83b52a515
--- /dev/null
+++ b/pytorch_forecasting/models/duet/sub_modules/layers/Embed.py
@@ -0,0 +1,208 @@
+import math
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn.utils import weight_norm
+
+
+class PositionalEmbedding(nn.Module):
+    def __init__(self, d_model, max_len=5000):
+        super().__init__()
+        # Compute the positional encodings once in log space.
+        pe = torch.zeros(max_len, d_model).float()
+        pe.require_grad = False
+
+        position = torch.arange(0, max_len).float().unsqueeze(1)
+        div_term = (
+            torch.arange(0, d_model, 2).float() * -(math.log(10000.0) / d_model)
+        ).exp()
+
+        pe[:, 0::2] = torch.sin(position * div_term)
+        pe[:, 1::2] = torch.cos(position * div_term)
+
+        pe = pe.unsqueeze(0)
+        self.register_buffer("pe", pe)
+
+    def forward(self, x):
+        return self.pe[:, : x.size(1)]
+
+
+class TokenEmbedding(nn.Module):
+    def __init__(self, c_in, d_model):
+        super().__init__()
+        padding = 1 if torch.__version__ >= "1.5.0" else 2
+        self.tokenConv = nn.Conv1d(
+            in_channels=c_in,
+            out_channels=d_model,
+            kernel_size=3,
+            padding=padding,
+            padding_mode="circular",
+            bias=False,
+        )
+        for m in self.modules():
+            if isinstance(m, nn.Conv1d):
+                nn.init.kaiming_normal_(
+                    m.weight, mode="fan_in", nonlinearity="leaky_relu"
+                )
+
+    def forward(self, x):
+        x = self.tokenConv(x.permute(0, 2, 1)).transpose(1, 2)
+        return x
+
+
+class FixedEmbedding(nn.Module):
+    def __init__(self, c_in, d_model):
+        super().__init__()
+
+        w = torch.zeros(c_in, d_model).float()
+        w.require_grad = False
+
+        position = torch.arange(0, c_in).float().unsqueeze(1)
+        div_term = (
+            torch.arange(0, d_model, 2).float() * -(math.log(10000.0) / d_model)
+        ).exp()
+
+        w[:, 0::2] = torch.sin(position * div_term)
+        w[:, 1::2] = torch.cos(position * div_term)
+
+        self.emb = nn.Embedding(c_in, d_model)
+        self.emb.weight = nn.Parameter(w, requires_grad=False)
+
+    def forward(self, x):
+        return self.emb(x).detach()
+
+
+class TemporalEmbedding(nn.Module):
+    def __init__(self, d_model, embed_type="fixed", freq="h"):
+        super().__init__()
+
+        minute_size = 4
+        hour_size = 24
+        weekday_size = 7
+        day_size = 32
+        month_size = 13
+
+        Embed = FixedEmbedding if embed_type == "fixed" else nn.Embedding
+        if freq == "t":
+            self.minute_embed = Embed(minute_size, d_model)
+        self.hour_embed = Embed(hour_size, d_model)
+        self.weekday_embed = Embed(weekday_size, d_model)
+        self.day_embed = Embed(day_size, d_model)
+        self.month_embed = Embed(month_size, d_model)
+
+    def forward(self, x):
+        x = x.long()
+        minute_x = (
+            self.minute_embed(x[:, :, 4]) if hasattr(self, "minute_embed") else 0.0
+        )
+        hour_x = self.hour_embed(x[:, :, 3])
+        weekday_x = self.weekday_embed(x[:, :, 2])
+        day_x = self.day_embed(x[:, :, 1])
+        month_x = self.month_embed(x[:, :, 0])
+
+        return hour_x + weekday_x + day_x + month_x + minute_x
+
+
+class TimeFeatureEmbedding(nn.Module):
+    def __init__(self, d_model, embed_type="timeF", freq="h"):
+        super().__init__()
+
+        freq_map = {"h": 4, "t": 5, "s": 6, "m": 1, "a": 1, "w": 2, "d": 3, "b": 3}
+        d_inp = freq_map[freq]
+        self.embed = nn.Linear(d_inp, d_model, bias=False)
+
+    def forward(self, x):
+        return self.embed(x)
+
+
+class DataEmbedding(nn.Module):
+    def __init__(self, c_in, d_model, embed_type="fixed", freq="h", dropout=0.1):
+        super().__init__()
+
+        self.value_embedding = TokenEmbedding(c_in=c_in, d_model=d_model)
+        self.position_embedding = PositionalEmbedding(d_model=d_model)
+        self.temporal_embedding = (
+            TemporalEmbedding(d_model=d_model, embed_type=embed_type, freq=freq)
+            if embed_type != "timeF"
+            else TimeFeatureEmbedding(d_model=d_model, embed_type=embed_type, freq=freq)
+        )
+        self.dropout = nn.Dropout(p=dropout)
+
+    def forward(self, x, x_mark):
+        if x_mark is None:
+            x = self.value_embedding(x) + self.position_embedding(x)
+        else:
+            x = (
+                self.value_embedding(x)
+                + self.temporal_embedding(x_mark)
+                + self.position_embedding(x)
+            )
+        return self.dropout(x)
+
+
+class DataEmbedding_inverted(nn.Module):
+    def __init__(self, c_in, d_model, embed_type="fixed", freq="h", dropout=0.1):
+        super().__init__()
+        self.value_embedding = nn.Linear(c_in, d_model)
+        self.dropout = nn.Dropout(p=dropout)
+
+    def forward(self, x, x_mark):
+        x = x.permute(0, 2, 1)
+        # x: [Batch Variate Time]
+        if x_mark is None:
+            x = self.value_embedding(x)
+        else:
+            x = self.value_embedding(torch.cat([x, x_mark.permute(0, 2, 1)], 1))
+        # x: [Batch Variate d_model]
+        return self.dropout(x)
+
+
+class DataEmbedding_wo_pos(nn.Module):
+    def __init__(self, c_in, d_model, embed_type="fixed", freq="h", dropout=0.1):
+        super().__init__()
+
+        self.value_embedding = TokenEmbedding(c_in=c_in, d_model=d_model)
+        self.position_embedding = PositionalEmbedding(d_model=d_model)
+        self.temporal_embedding = (
+            TemporalEmbedding(d_model=d_model, embed_type=embed_type, freq=freq)
+            if embed_type != "timeF"
+            else TimeFeatureEmbedding(d_model=d_model, embed_type=embed_type, freq=freq)
+        )
+        self.dropout = nn.Dropout(p=dropout)
+
+    def forward(self, x, x_mark):
+        if x_mark is None:
+            x = self.value_embedding(x)
+        else:
+            x = self.value_embedding(x) + self.temporal_embedding(x_mark)
+        return self.dropout(x)
+
+
+class PatchEmbedding(nn.Module):
+    def __init__(self, d_model, patch_len, stride, padding, dropout):
+        super().__init__()
+        # Patching
+        self.patch_len = patch_len
+        self.stride = stride
+        self.padding_patch_layer = nn.ReplicationPad1d((0, padding))
+
+        # Backbone, Input encoding: projection of feature vectors
+        # onto a d-dim vector space
+        self.value_embedding = nn.Linear(patch_len, d_model, bias=False)
+
+        # Positional embedding
+        self.position_embedding = PositionalEmbedding(d_model)
+
+        # Residual dropout
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x):
+        # do patching
+        n_vars = x.shape[1]
+        x = self.padding_patch_layer(x)
+        x = x.unfold(dimension=-1, size=self.patch_len, step=self.stride)
+        x = torch.reshape(x, (x.shape[0] * x.shape[1], x.shape[2], x.shape[3]))
+        # Input encoding
+        x = self.value_embedding(x) + self.position_embedding(x)
+        return self.dropout(x), n_vars
diff --git a/pytorch_forecasting/models/duet/sub_modules/layers/RevIN.py b/pytorch_forecasting/models/duet/sub_modules/layers/RevIN.py
new file mode 100644
index 000000000..9364115d9
--- /dev/null
+++ b/pytorch_forecasting/models/duet/sub_modules/layers/RevIN.py
@@ -0,0 +1,67 @@
+import torch
+import torch.nn as nn
+
+
+class RevIN(nn.Module):
+    def __init__(self, num_features: int, eps=1e-5, affine=True, subtract_last=False):
+        """
+        :param num_features: the number of features or channels
+        :param eps: a value added for numerical stability
+        :param affine: if True, RevIN has learnable affine parameters
+        """
+        super().__init__()
+        self.num_features = num_features
+        self.eps = eps
+        self.affine = affine
+        self.subtract_last = subtract_last
+        if self.affine:
+            self._init_params()
+
+    def forward(self, x, mode: str):
+        if mode == "norm":
+            self._get_statistics(x)
+            x = self._normalize(x)
+        elif mode == "denorm":
+            x = self._denormalize(x)
+        else:
+            raise NotImplementedError
+        return x
+
+    def _init_params(self):
+        # initialize RevIN params: (C,)
+        self.affine_weight = nn.Parameter(torch.ones(self.num_features))
+        self.affine_bias = nn.Parameter(torch.zeros(self.num_features))
+
+    def _get_statistics(self, x):
+        dim2reduce = tuple(range(1, x.ndim - 1))
+        if self.subtract_last:
+            self.last = x[:, -1, :].unsqueeze(1)
+        else:
+            self.mean = torch.mean(
+                x, dim=dim2reduce, keepdim=True
+            ).detach()  # 沿时间维度求平均，得到各通道的时间均值
+        self.stdev = torch.sqrt(
+            torch.var(x, dim=dim2reduce, keepdim=True, unbiased=False) + self.eps
+        ).detach()
+
+    def _normalize(self, x):
+        if self.subtract_last:
+            x = x - self.last
+        else:
+            x = x - self.mean
+        x = x / self.stdev
+        if self.affine:
+            x = x * self.affine_weight
+            x = x + self.affine_bias
+        return x
+
+    def _denormalize(self, x):
+        if self.affine:
+            x = x - self.affine_bias
+            x = x / (self.affine_weight + self.eps * self.eps)
+        x = x * self.stdev
+        if self.subtract_last:
+            x = x + self.last
+        else:
+            x = x + self.mean
+        return x
diff --git a/pytorch_forecasting/models/duet/sub_modules/layers/SelfAttention_Family.py b/pytorch_forecasting/models/duet/sub_modules/layers/SelfAttention_Family.py
new file mode 100644
index 000000000..1da80bd48
--- /dev/null
+++ b/pytorch_forecasting/models/duet/sub_modules/layers/SelfAttention_Family.py
@@ -0,0 +1,371 @@
+from math import sqrt
+
+from pytorch_forecasting.utils._dependencies._safe_import import _safe_import
+
+rearrange = _safe_import("einops.rearrange")
+repeat = _safe_import("einops.repeat")
+LSHSelfAttention = _safe_import("reformer_pytorch.LSHSelfAttention")
+
+import numpy as np
+import torch
+import torch.nn as nn
+
+from pytorch_forecasting.models.duet.sub_modules.utils.masking import (
+    ProbMask,
+    TriangularCausalMask,
+)
+
+
+class DSAttention(nn.Module):
+    """De-stationary Attention"""
+
+    def __init__(
+        self,
+        mask_flag=True,
+        factor=5,
+        scale=None,
+        attention_dropout=0.1,
+        output_attention=False,
+    ):
+        super().__init__()
+        self.scale = scale
+        self.mask_flag = mask_flag
+        self.output_attention = output_attention
+        self.dropout = nn.Dropout(attention_dropout)
+
+    def forward(self, queries, keys, values, attn_mask, tau=None, delta=None):
+        B, L, H, E = queries.shape
+        _, S, _, D = values.shape
+        scale = self.scale or 1.0 / sqrt(E)
+
+        tau = 1.0 if tau is None else tau.unsqueeze(1).unsqueeze(1)  # B x 1 x 1 x 1
+        delta = (
+            0.0 if delta is None else delta.unsqueeze(1).unsqueeze(1)
+        )  # B x 1 x 1 x S
+
+        # De-stationary Attention, rescaling pre-softmax score with learned
+        # de-stationary factors
+        scores = torch.einsum("blhe,bshe->bhls", queries, keys) * tau + delta
+
+        if self.mask_flag:
+            if attn_mask is None:
+                attn_mask = TriangularCausalMask(B, L, device=queries.device)
+
+            scores.masked_fill_(attn_mask.mask, -np.inf)
+
+        A = self.dropout(torch.softmax(scale * scores, dim=-1))
+        V = torch.einsum("bhls,bshd->blhd", A, values)
+
+        if self.output_attention:
+            return V.contiguous(), A
+        else:
+            return V.contiguous(), None
+
+
+class FullAttention(nn.Module):
+    def __init__(
+        self,
+        mask_flag=True,
+        factor=5,
+        scale=None,
+        attention_dropout=0.1,
+        output_attention=False,
+    ):
+        super().__init__()
+        self.scale = scale
+        self.mask_flag = mask_flag
+        self.output_attention = output_attention
+        self.dropout = nn.Dropout(attention_dropout)
+
+    def forward(self, queries, keys, values, attn_mask, tau=None, delta=None):
+        B, L, H, E = queries.shape
+        _, S, _, D = values.shape
+        scale = self.scale or 1.0 / sqrt(E)
+
+        scores = torch.einsum("blhe,bshe->bhls", queries, keys)
+
+        if self.mask_flag:
+            if attn_mask is None:
+                attn_mask = TriangularCausalMask(B, L, device=queries.device)
+
+            scores.masked_fill_(attn_mask.mask, -np.inf)
+
+        A = self.dropout(torch.softmax(scale * scores, dim=-1))
+        V = torch.einsum("bhls,bshd->blhd", A, values)
+
+        if self.output_attention:
+            return V.contiguous(), A
+        else:
+            return V.contiguous(), None
+
+
+class ProbAttention(nn.Module):
+    def __init__(
+        self,
+        mask_flag=True,
+        factor=5,
+        scale=None,
+        attention_dropout=0.1,
+        output_attention=False,
+    ):
+        super().__init__()
+        self.factor = factor
+        self.scale = scale
+        self.mask_flag = mask_flag
+        self.output_attention = output_attention
+        self.dropout = nn.Dropout(attention_dropout)
+
+    def _prob_QK(self, Q, K, sample_k, n_top):  # n_top: c*ln(L_q)
+        # Q [B, H, L, D]
+        B, H, L_K, E = K.shape
+        _, _, L_Q, _ = Q.shape
+
+        # calculate the sampled Q_K
+        K_expand = K.unsqueeze(-3).expand(B, H, L_Q, L_K, E)
+        # real U = U_part(factor*ln(L_k))*L_q
+        index_sample = torch.randint(L_K, (L_Q, sample_k))
+        K_sample = K_expand[:, :, torch.arange(L_Q).unsqueeze(1), index_sample, :]
+        Q_K_sample = torch.matmul(Q.unsqueeze(-2), K_sample.transpose(-2, -1)).squeeze()
+
+        # find the Top_k query with sparisty measurement
+        M = Q_K_sample.max(-1)[0] - torch.div(Q_K_sample.sum(-1), L_K)
+        M_top = M.topk(n_top, sorted=False)[1]
+
+        # use the reduced Q to calculate Q_K
+        Q_reduce = Q[
+            torch.arange(B)[:, None, None], torch.arange(H)[None, :, None], M_top, :
+        ]  # factor*ln(L_q)
+        Q_K = torch.matmul(Q_reduce, K.transpose(-2, -1))  # factor*ln(L_q)*L_k
+
+        return Q_K, M_top
+
+    def _get_initial_context(self, V, L_Q):
+        B, H, L_V, D = V.shape
+        if not self.mask_flag:
+            # V_sum = V.sum(dim=-2)
+            V_sum = V.mean(dim=-2)
+            contex = V_sum.unsqueeze(-2).expand(B, H, L_Q, V_sum.shape[-1]).clone()
+        else:  # use mask
+            # requires that L_Q == L_V, i.e. for self-attention only
+            assert L_Q == L_V
+            contex = V.cumsum(dim=-2)
+        return contex
+
+    def _update_context(self, context_in, V, scores, index, L_Q, attn_mask):
+        B, H, L_V, D = V.shape
+
+        if self.mask_flag:
+            attn_mask = ProbMask(B, H, L_Q, index, scores, device=V.device)
+            scores.masked_fill_(attn_mask.mask, -np.inf)
+
+        attn = torch.softmax(scores, dim=-1)  # nn.Softmax(dim=-1)(scores)
+
+        context_in[
+            torch.arange(B)[:, None, None], torch.arange(H)[None, :, None], index, :
+        ] = torch.matmul(attn, V).type_as(context_in)
+        if self.output_attention:
+            attns = (torch.ones([B, H, L_V, L_V]) / L_V).type_as(attn).to(attn.device)
+            attns[
+                torch.arange(B)[:, None, None], torch.arange(H)[None, :, None], index, :
+            ] = attn
+            return context_in, attns
+        else:
+            return context_in, None
+
+    def forward(self, queries, keys, values, attn_mask, tau=None, delta=None):
+        B, L_Q, H, D = queries.shape
+        _, L_K, _, _ = keys.shape
+
+        queries = queries.transpose(2, 1)
+        keys = keys.transpose(2, 1)
+        values = values.transpose(2, 1)
+
+        U_part = self.factor * np.ceil(np.log(L_K)).astype("int").item()  # c*ln(L_k)
+        u = self.factor * np.ceil(np.log(L_Q)).astype("int").item()  # c*ln(L_q)
+
+        U_part = U_part if U_part < L_K else L_K
+        u = u if u < L_Q else L_Q
+
+        scores_top, index = self._prob_QK(queries, keys, sample_k=U_part, n_top=u)
+
+        # add scale factor
+        scale = self.scale or 1.0 / sqrt(D)
+        if scale is not None:
+            scores_top = scores_top * scale
+        # get the context
+        context = self._get_initial_context(values, L_Q)
+        # update the context with selected top_k queries
+        context, attn = self._update_context(
+            context, values, scores_top, index, L_Q, attn_mask
+        )
+
+        return context.contiguous(), attn
+
+
+class AttentionLayer(nn.Module):
+    def __init__(self, attention, d_model, n_heads, d_keys=None, d_values=None):
+        super().__init__()
+
+        d_keys = d_keys or (d_model // n_heads)
+        d_values = d_values or (d_model // n_heads)
+
+        self.inner_attention = attention
+        self.query_projection = nn.Linear(d_model, d_keys * n_heads)
+        self.key_projection = nn.Linear(d_model, d_keys * n_heads)
+        self.value_projection = nn.Linear(d_model, d_values * n_heads)
+        self.out_projection = nn.Linear(d_values * n_heads, d_model)
+        self.n_heads = n_heads
+
+    def forward(self, queries, keys, values, attn_mask, tau=None, delta=None):
+        B, L, _ = queries.shape
+        _, S, _ = keys.shape
+        H = self.n_heads
+
+        queries = self.query_projection(queries).view(B, L, H, -1)
+        keys = self.key_projection(keys).view(B, S, H, -1)
+        values = self.value_projection(values).view(B, S, H, -1)
+
+        out, attn = self.inner_attention(
+            queries, keys, values, attn_mask, tau=tau, delta=delta
+        )
+        out = out.view(B, L, -1)
+
+        return self.out_projection(out), attn
+
+
+class ReformerLayer(nn.Module):
+    def __init__(
+        self,
+        attention,
+        d_model,
+        n_heads,
+        d_keys=None,
+        d_values=None,
+        causal=False,
+        bucket_size=4,
+        n_hashes=4,
+    ):
+        super().__init__()
+        self.bucket_size = bucket_size
+        self.attn = LSHSelfAttention(
+            dim=d_model,
+            heads=n_heads,
+            bucket_size=bucket_size,
+            n_hashes=n_hashes,
+            causal=causal,
+        )
+
+    def fit_length(self, queries):
+        # inside reformer: assert N % (bucket_size * 2) == 0
+        B, N, C = queries.shape
+        if N % (self.bucket_size * 2) == 0:
+            return queries
+        else:
+            # fill the time data
+            fill_len = (self.bucket_size * 2) - (N % (self.bucket_size * 2))
+            return torch.cat(
+                [queries, torch.zeros([B, fill_len, C]).to(queries.device)], dim=1
+            )
+
+    def forward(self, queries, keys, values, attn_mask, tau, delta):
+        # in Reformer: defalut queries=keys
+        B, N, C = queries.shape
+        queries = self.attn(self.fit_length(queries))[:, :N, :]
+        return queries, None
+
+
+class TwoStageAttentionLayer(nn.Module):
+    """
+    The Two Stage Attention (TSA) Layer
+    input/output shape: [batch_size, Data_dim(D), Seg_num(L), d_model]
+    """
+
+    def __init__(
+        self, configs, seg_num, factor, d_model, n_heads, d_ff=None, dropout=0.1
+    ):
+        super().__init__()
+        d_ff = d_ff or 4 * d_model
+        self.time_attention = AttentionLayer(
+            FullAttention(
+                False,
+                configs.factor,
+                attention_dropout=configs.dropout,
+                output_attention=configs.output_attention,
+            ),
+            d_model,
+            n_heads,
+        )
+        self.dim_sender = AttentionLayer(
+            FullAttention(
+                False,
+                configs.factor,
+                attention_dropout=configs.dropout,
+                output_attention=configs.output_attention,
+            ),
+            d_model,
+            n_heads,
+        )
+        self.dim_receiver = AttentionLayer(
+            FullAttention(
+                False,
+                configs.factor,
+                attention_dropout=configs.dropout,
+                output_attention=configs.output_attention,
+            ),
+            d_model,
+            n_heads,
+        )
+        self.router = nn.Parameter(torch.randn(seg_num, factor, d_model))
+
+        self.dropout = nn.Dropout(dropout)
+
+        self.norm1 = nn.LayerNorm(d_model)
+        self.norm2 = nn.LayerNorm(d_model)
+        self.norm3 = nn.LayerNorm(d_model)
+        self.norm4 = nn.LayerNorm(d_model)
+
+        self.MLP1 = nn.Sequential(
+            nn.Linear(d_model, d_ff), nn.GELU(), nn.Linear(d_ff, d_model)
+        )
+        self.MLP2 = nn.Sequential(
+            nn.Linear(d_model, d_ff), nn.GELU(), nn.Linear(d_ff, d_model)
+        )
+
+    def forward(self, x, attn_mask=None, tau=None, delta=None):
+        # Cross Time Stage: Directly apply MSA to each dimension
+        batch = x.shape[0]
+        time_in = rearrange(x, "b ts_d seg_num d_model -> (b ts_d) seg_num d_model")
+        time_enc, attn = self.time_attention(
+            time_in, time_in, time_in, attn_mask=None, tau=None, delta=None
+        )
+        dim_in = time_in + self.dropout(time_enc)
+        dim_in = self.norm1(dim_in)
+        dim_in = dim_in + self.dropout(self.MLP1(dim_in))
+        dim_in = self.norm2(dim_in)
+
+        # Cross Dimension Stage: use a small set of learnable vectors to aggregate and
+        # distribute messages to build the D-to-D connection
+        dim_send = rearrange(
+            dim_in, "(b ts_d) seg_num d_model -> (b seg_num) ts_d d_model", b=batch
+        )
+        batch_router = repeat(
+            self.router,
+            "seg_num factor d_model -> (repeat seg_num) factor d_model",
+            repeat=batch,
+        )
+        dim_buffer, attn = self.dim_sender(
+            batch_router, dim_send, dim_send, attn_mask=None, tau=None, delta=None
+        )
+        dim_receive, attn = self.dim_receiver(
+            dim_send, dim_buffer, dim_buffer, attn_mask=None, tau=None, delta=None
+        )
+        dim_enc = dim_send + self.dropout(dim_receive)
+        dim_enc = self.norm3(dim_enc)
+        dim_enc = dim_enc + self.dropout(self.MLP2(dim_enc))
+        dim_enc = self.norm4(dim_enc)
+
+        final_out = rearrange(
+            dim_enc, "(b seg_num) ts_d d_model -> b ts_d seg_num d_model", b=batch
+        )
+
+        return final_out
diff --git a/pytorch_forecasting/models/duet/sub_modules/layers/Transformer_EncDec.py b/pytorch_forecasting/models/duet/sub_modules/layers/Transformer_EncDec.py
new file mode 100644
index 000000000..412ec07ce
--- /dev/null
+++ b/pytorch_forecasting/models/duet/sub_modules/layers/Transformer_EncDec.py
@@ -0,0 +1,144 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class ConvLayer(nn.Module):
+    def __init__(self, c_in):
+        super().__init__()
+        self.downConv = nn.Conv1d(
+            in_channels=c_in,
+            out_channels=c_in,
+            kernel_size=3,
+            padding=2,
+            padding_mode="circular",
+        )
+        self.norm = nn.BatchNorm1d(c_in)
+        self.activation = nn.ELU()
+        self.maxPool = nn.MaxPool1d(kernel_size=3, stride=2, padding=1)
+
+    def forward(self, x):
+        x = self.downConv(x.permute(0, 2, 1))
+        x = self.norm(x)
+        x = self.activation(x)
+        x = self.maxPool(x)
+        x = x.transpose(1, 2)
+        return x
+
+
+class EncoderLayer(nn.Module):
+    def __init__(self, attention, d_model, d_ff=None, dropout=0.1, activation="relu"):
+        super().__init__()
+        d_ff = d_ff or 4 * d_model
+        self.attention = attention
+        self.conv1 = nn.Conv1d(in_channels=d_model, out_channels=d_ff, kernel_size=1)
+        self.conv2 = nn.Conv1d(in_channels=d_ff, out_channels=d_model, kernel_size=1)
+        self.norm1 = nn.LayerNorm(d_model)
+        self.norm2 = nn.LayerNorm(d_model)
+        self.dropout = nn.Dropout(dropout)
+        self.activation = F.relu if activation == "relu" else F.gelu
+
+    def forward(self, x, attn_mask=None, tau=None, delta=None):
+        new_x, attn = self.attention(x, x, x, attn_mask=attn_mask, tau=tau, delta=delta)
+        x = x + self.dropout(new_x)
+
+        y = x = self.norm1(x)
+        y = self.dropout(self.activation(self.conv1(y.transpose(-1, 1))))
+        y = self.dropout(self.conv2(y).transpose(-1, 1))
+
+        return self.norm2(x + y), attn
+
+
+class Encoder(nn.Module):
+    def __init__(self, attn_layers, conv_layers=None, norm_layer=None):
+        super().__init__()
+        self.attn_layers = nn.ModuleList(attn_layers)
+        self.conv_layers = (
+            nn.ModuleList(conv_layers) if conv_layers is not None else None
+        )
+        self.norm = norm_layer
+
+    def forward(self, x, attn_mask=None, tau=None, delta=None):
+        # x [B, L, D]
+        attns = []
+        if self.conv_layers is not None:
+            for i, (attn_layer, conv_layer) in enumerate(
+                zip(self.attn_layers, self.conv_layers)
+            ):
+                delta = delta if i == 0 else None
+                x, attn = attn_layer(x, attn_mask=attn_mask, tau=tau, delta=delta)
+                x = conv_layer(x)
+                attns.append(attn)
+            x, attn = self.attn_layers[-1](x, tau=tau, delta=None)
+            attns.append(attn)
+        else:
+            for attn_layer in self.attn_layers:
+                x, attn = attn_layer(x, attn_mask=attn_mask, tau=tau, delta=delta)
+                attns.append(attn)
+
+        if self.norm is not None:
+            x = self.norm(x)
+
+        return x, attns
+
+
+class DecoderLayer(nn.Module):
+    def __init__(
+        self,
+        self_attention,
+        cross_attention,
+        d_model,
+        d_ff=None,
+        dropout=0.1,
+        activation="relu",
+    ):
+        super().__init__()
+        d_ff = d_ff or 4 * d_model
+        self.self_attention = self_attention
+        self.cross_attention = cross_attention
+        self.conv1 = nn.Conv1d(in_channels=d_model, out_channels=d_ff, kernel_size=1)
+        self.conv2 = nn.Conv1d(in_channels=d_ff, out_channels=d_model, kernel_size=1)
+        self.norm1 = nn.LayerNorm(d_model)
+        self.norm2 = nn.LayerNorm(d_model)
+        self.norm3 = nn.LayerNorm(d_model)
+        self.dropout = nn.Dropout(dropout)
+        self.activation = F.relu if activation == "relu" else F.gelu
+
+    def forward(self, x, cross, x_mask=None, cross_mask=None, tau=None, delta=None):
+        x = x + self.dropout(
+            self.self_attention(x, x, x, attn_mask=x_mask, tau=tau, delta=None)[0]
+        )
+        x = self.norm1(x)
+
+        x = x + self.dropout(
+            self.cross_attention(
+                x, cross, cross, attn_mask=cross_mask, tau=tau, delta=delta
+            )[0]
+        )
+
+        y = x = self.norm2(x)
+        y = self.dropout(self.activation(self.conv1(y.transpose(-1, 1))))
+        y = self.dropout(self.conv2(y).transpose(-1, 1))
+
+        return self.norm3(x + y)
+
+
+class Decoder(nn.Module):
+    def __init__(self, layers, norm_layer=None, projection=None):
+        super().__init__()
+        self.layers = nn.ModuleList(layers)
+        self.norm = norm_layer
+        self.projection = projection
+
+    def forward(self, x, cross, x_mask=None, cross_mask=None, tau=None, delta=None):
+        for layer in self.layers:
+            x = layer(
+                x, cross, x_mask=x_mask, cross_mask=cross_mask, tau=tau, delta=delta
+            )
+
+        if self.norm is not None:
+            x = self.norm(x)
+
+        if self.projection is not None:
+            x = self.projection(x)
+        return x
diff --git a/pytorch_forecasting/models/duet/sub_modules/layers/__init__.py b/pytorch_forecasting/models/duet/sub_modules/layers/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/pytorch_forecasting/models/duet/sub_modules/layers/distributional_router_encoder.py b/pytorch_forecasting/models/duet/sub_modules/layers/distributional_router_encoder.py
new file mode 100644
index 000000000..0f8bd0bb7
--- /dev/null
+++ b/pytorch_forecasting/models/duet/sub_modules/layers/distributional_router_encoder.py
@@ -0,0 +1,21 @@
+import torch
+import torch.nn as nn
+
+
+class encoder(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        input_size = config.seq_len
+        num_experts = config.num_experts
+        encoder_hidden_size = config.hidden_size
+
+        self.distribution_fit = nn.Sequential(
+            nn.Linear(input_size, encoder_hidden_size, bias=False),
+            nn.ReLU(),
+            nn.Linear(encoder_hidden_size, num_experts, bias=False),
+        )
+
+    def forward(self, x):
+        mean = torch.mean(x, dim=-1)
+        out = self.distribution_fit(mean)
+        return out
diff --git a/pytorch_forecasting/models/duet/sub_modules/layers/linear_extractor_cluster.py b/pytorch_forecasting/models/duet/sub_modules/layers/linear_extractor_cluster.py
new file mode 100644
index 000000000..d0c18eaaf
--- /dev/null
+++ b/pytorch_forecasting/models/duet/sub_modules/layers/linear_extractor_cluster.py
@@ -0,0 +1,294 @@
+from pytorch_forecasting.utils._dependencies._safe_import import _safe_import
+
+rearrange = _safe_import("einops.rearrange")
+import torch
+from torch.distributions.normal import Normal
+import torch.nn as nn
+
+from ..layers.RevIN import RevIN
+from .distributional_router_encoder import encoder
+from .linear_pattern_extractor import Linear_extractor as expert
+
+
+class SparseDispatcher:
+    """Helper for implementing a mixture of experts.
+    The purpose of this class is to create input mini-batches for the
+    experts and to combine the results of the experts to form a unified
+    output tensor.
+    There are two functions:
+    dispatch - take an input Tensor and create input Tensors for each expert.
+    combine - take output Tensors from each expert and form a combined output
+      Tensor.  Outputs from different experts for the same batch element are
+      summed together, weighted by the provided "gates".
+    The class is initialized with a "gates" Tensor, which specifies which
+    batch elements go to which experts, and the weights to use when combining
+    the outputs.  Batch element b is sent to expert e iff gates[b, e] != 0.
+    The inputs and outputs are all two-dimensional [batch, depth].
+    Caller is responsible for collapsing additional dimensions prior to
+    calling this class and reshaping the output to the original shape.
+    See common_layers.reshape_like().
+    Example use:
+    gates: a float32 `Tensor` with shape `[batch_size, num_experts]`
+    inputs: a float32 `Tensor` with shape `[batch_size, input_size]`
+    experts: a list of length `num_experts` containing sub-networks.
+    dispatcher = SparseDispatcher(num_experts, gates)
+    expert_inputs = dispatcher.dispatch(inputs)
+    expert_outputs = [experts[i](expert_inputs[i]) for i in range(num_experts)]
+    outputs = dispatcher.combine(expert_outputs)
+    The preceding code sets the output for a particular example b to:
+    output[b] = Sum_i(gates[b, i] * experts[i](inputs[b]))
+    This class takes advantage of sparsity in the gate matrix by including in the
+    `Tensor`s for expert i only the batch elements for which `gates[b, i] > 0`.
+    """
+
+    def __init__(self, num_experts, gates):
+        """Create a SparseDispatcher."""
+
+        self._gates = gates
+        self._num_experts = num_experts
+        # sort experts
+        sorted_experts, index_sorted_experts = torch.nonzero(gates).sort(0)
+        # drop indices
+        _, self._expert_index = sorted_experts.split(1, dim=1)
+        # get according batch index for each expert
+        self._batch_index = torch.nonzero(gates)[index_sorted_experts[:, 1], 0]
+        # calculate num samples that each expert gets
+        self._part_sizes = (gates > 0).sum(0).tolist()
+        # expand gates to match with self._batch_index
+        gates_exp = gates[self._batch_index.flatten()]
+        self._nonzero_gates = torch.gather(gates_exp, 1, self._expert_index)
+
+    def dispatch(self, inp):
+        """Create one input Tensor for each expert.
+        The `Tensor` for an expert `i` contains the slices of `inp` corresponding
+        to the batch elements `b` where `gates[b, i] > 0`.
+        Args:
+          inp: a `Tensor` of shape "[batch_size, <extra_input_dims>]`
+        Returns:
+          a list of `num_experts` `Tensor`s with shapes
+            `[expert_batch_size_i, <extra_input_dims>]`.
+        """
+
+        # assigns samples to experts whose gate is nonzero
+
+        # expand according to batch index so we can just split by _part_sizes
+        inp_exp = inp[self._batch_index].squeeze(1)
+        return torch.split(inp_exp, self._part_sizes, dim=0)
+
+    def combine(self, expert_out, multiply_by_gates=True):
+        """Sum together the expert output, weighted by the gates.
+        The slice corresponding to a particular batch element `b` is computed
+        as the sum over all experts `i` of the expert output, weighted by the
+        corresponding gate values.  If `multiply_by_gates` is set to False, the
+        gate values are ignored.
+        Args:
+          expert_out: a list of `num_experts` `Tensor`s, each with shape
+            `[expert_batch_size_i, <extra_output_dims>]`.
+          multiply_by_gates: a boolean
+        Returns:
+          a `Tensor` with shape `[batch_size, <extra_output_dims>]`.
+        """
+        # apply exp to expert outputs, so we are not longer in log space
+        stitched = torch.cat(expert_out, 0)
+        if multiply_by_gates:
+            # stitched = stitched.mul(self._nonzero_gates)
+            stitched = torch.einsum("i...,ij->i...", stitched, self._nonzero_gates)
+
+        shape = list(expert_out[-1].shape)
+        shape[0] = self._gates.size(0)
+        zeros = torch.zeros(*shape, requires_grad=True, device=stitched.device)
+        # combine samples that have been processed by the same k experts
+        combined = zeros.index_add(0, self._batch_index, stitched.float())
+        return combined
+
+    def expert_to_gates(self):
+        """Gate values corresponding to the examples in the per-expert `Tensor`s.
+        Returns:
+          a list of `num_experts` one-dimensional `Tensor`s with type `tf.float32`
+              and shapes `[expert_batch_size_i]`
+        """
+        # split nonzero gates for each expert
+        return torch.split(self._nonzero_gates, self._part_sizes, dim=0)
+
+
+class Linear_extractor_cluster(nn.Module):
+    """Call a Sparsely gated mixture of experts layer with 1-layer Feed-Forward networks
+       as experts.
+    Args:
+    input_size: integer - size of the input
+    output_size: integer - size of the input
+    num_experts: an integer - number of experts
+    hidden_size: an integer - hidden size of the experts
+    noisy_gating: a boolean
+    k: an integer - how many experts to use for each batch element
+    """
+
+    def __init__(self, config):
+        super().__init__()
+        self.noisy_gating = config.noisy_gating
+        self.num_experts = config.num_experts
+        self.input_size = config.seq_len
+        self.k = config.k
+        # instantiate experts
+        self.experts = nn.ModuleList([expert(config) for _ in range(self.num_experts)])
+        self.W_h = nn.Parameter(torch.eye(self.num_experts))
+        self.gate = encoder(config)
+        self.noise = encoder(config)
+
+        self.n_vars = config.enc_in
+        self.revin = RevIN(self.n_vars)
+
+        self.CI = config.CI
+        self.softplus = nn.Softplus()
+        self.softmax = nn.Softmax(1)
+        self.register_buffer("mean", torch.tensor([0.0]))
+        self.register_buffer("std", torch.tensor([1.0]))
+        assert self.k <= self.num_experts
+
+    def cv_squared(self, x):
+        """The squared coefficient of variation of a sample.
+        Useful as a loss to encourage a positive distribution to be more uniform.
+        Epsilons added for numerical stability.
+        Returns 0 for an empty Tensor.
+        Args:
+        x: a `Tensor`.
+        Returns:
+        a `Scalar`.
+        """
+        eps = 1e-10
+        # if only num_experts = 1
+
+        if x.shape[0] == 1:
+            return torch.tensor([0], device=x.device, dtype=x.dtype)
+        return x.float().var() / (x.float().mean() ** 2 + eps)
+
+    def _gates_to_load(self, gates):
+        """Compute the true load per expert, given the gates.
+        The load is the number of examples for which the corresponding gate is >0.
+        Args:
+        gates: a `Tensor` of shape [batch_size, n]
+        Returns:
+        a float32 `Tensor` of shape [n]
+        """
+        return (gates > 0).sum(0)
+
+    def _prob_in_top_k(
+        self, clean_values, noisy_values, noise_stddev, noisy_top_values
+    ):
+        """Helper function to NoisyTopKGating.
+        Computes the probability that value is in top k, given different random noise.
+        This gives us a way of backpropagating from a loss that balances the number
+        of times each expert is in the top k experts per example.
+        In the case of no noise, pass in None for noise_stddev, and the result will
+        not be differentiable.
+        Args:
+        clean_values: a `Tensor` of shape [batch, n].
+        noisy_values: a `Tensor` of shape [batch, n].  Equal to clean values plus
+          normally distributed noise with standard deviation noise_stddev.
+        noise_stddev: a `Tensor` of shape [batch, n], or None
+        noisy_top_values: a `Tensor` of shape [batch, m].
+           "values" Output of tf.top_k(noisy_top_values, m).  m >= k+1
+        Returns:
+        a `Tensor` of shape [batch, n].
+        """
+        batch = clean_values.size(0)
+        m = noisy_top_values.size(1)
+        top_values_flat = noisy_top_values.flatten()
+
+        threshold_positions_if_in = (
+            torch.arange(batch, device=clean_values.device) * m + self.k
+        )
+        threshold_if_in = torch.unsqueeze(
+            torch.gather(top_values_flat, 0, threshold_positions_if_in), 1
+        )
+        is_in = torch.gt(noisy_values, threshold_if_in)
+        threshold_positions_if_out = threshold_positions_if_in - 1
+        threshold_if_out = torch.unsqueeze(
+            torch.gather(top_values_flat, 0, threshold_positions_if_out), 1
+        )
+        # is each value currently in the top k.
+        normal = Normal(self.mean, self.std)
+        prob_if_in = normal.cdf((clean_values - threshold_if_in) / noise_stddev)
+        prob_if_out = normal.cdf((clean_values - threshold_if_out) / noise_stddev)
+        prob = torch.where(is_in, prob_if_in, prob_if_out)
+        return prob
+
+    def noisy_top_k_gating(self, x, train, noise_epsilon=1e-2):
+        """Noisy top-k gating.
+        See paper: https://arxiv.org/abs/1701.06538.
+        Args:
+          x: input Tensor with shape [batch_size, input_size]
+          train: a boolean - we only add noise at training time.
+          noise_epsilon: a float
+        Returns:
+          gates: a Tensor with shape [batch_size, num_experts]
+          load: a Tensor with shape [num_experts]
+        """
+        clean_logits = self.gate(x)
+
+        if self.noisy_gating and train:
+            raw_noise_stddev = self.noise(x)
+            noise_stddev = self.softplus(raw_noise_stddev) + noise_epsilon
+            noise = torch.randn_like(clean_logits)
+            noisy_logits = clean_logits + (noise * noise_stddev)
+            logits = noisy_logits @ self.W_h
+        else:
+            logits = clean_logits
+
+        # calculate topk + 1 that will be needed for the noisy gates
+        logits = self.softmax(logits)
+        top_logits, top_indices = logits.topk(min(self.k + 1, self.num_experts), dim=1)
+        top_k_logits = top_logits[:, : self.k]
+        top_k_indices = top_indices[:, : self.k]
+        top_k_gates = top_k_logits / (
+            top_k_logits.sum(1, keepdim=True) + 1e-6
+        )  # normalization
+
+        zeros = torch.zeros_like(logits, requires_grad=True)
+        gates = zeros.scatter(1, top_k_indices, top_k_gates)
+
+        if self.noisy_gating and self.k < self.num_experts and train:
+            load = (
+                self._prob_in_top_k(
+                    clean_logits, noisy_logits, noise_stddev, top_logits
+                )
+            ).sum(0)
+        else:
+            load = self._gates_to_load(gates)
+        return gates, load
+
+    def forward(self, x, loss_coef=1):
+        """Args:
+        x: tensor shape [batch_size, input_size]
+        train: a boolean scalar.
+        loss_coef: a scalar - multiplier on load-balancing losses
+
+        Returns:
+        y: a tensor with shape [batch_size, output_size].
+        extra_training_loss: a scalar.  This should be added into the overall
+        training loss of the model.  The backpropagation of this loss
+        encourages all experts to be approximately equally used across a batch.
+        """
+        gates, load = self.noisy_top_k_gating(x, self.training)
+        # calculate importance loss
+        importance = gates.sum(0)
+        loss = self.cv_squared(importance) + self.cv_squared(load)
+        loss *= loss_coef
+
+        dispatcher = SparseDispatcher(self.num_experts, gates)
+        if self.CI:
+            x_norm = rearrange(x, "(x y) l c -> x l (y c)", y=self.n_vars)
+            x_norm = self.revin(x_norm, "norm")
+            x_norm = rearrange(x_norm, "x l (y c) -> (x y) l c", y=self.n_vars)
+        else:
+            x_norm = self.revin(x, "norm")
+
+        expert_inputs = dispatcher.dispatch(x_norm)
+
+        gates = dispatcher.expert_to_gates()
+        expert_outputs = [
+            self.experts[i](expert_inputs[i]) for i in range(self.num_experts)
+        ]
+        y = dispatcher.combine(expert_outputs)
+
+        return y, loss
diff --git a/pytorch_forecasting/models/duet/sub_modules/layers/linear_pattern_extractor.py b/pytorch_forecasting/models/duet/sub_modules/layers/linear_pattern_extractor.py
new file mode 100644
index 000000000..1d67ad8ce
--- /dev/null
+++ b/pytorch_forecasting/models/duet/sub_modules/layers/linear_pattern_extractor.py
@@ -0,0 +1,83 @@
+import torch
+import torch.nn as nn
+
+from ..layers.Autoformer_EncDec import series_decomp
+
+
+class Linear_extractor(nn.Module):
+    """
+    Paper link: https://arxiv.org/pdf/2205.13504.pdf
+    """
+
+    def __init__(self, configs, individual=False):
+        """
+        individual: Bool, whether shared model among different variates.
+        """
+        super().__init__()
+        self.seq_len = configs.seq_len
+
+        self.pred_len = configs.d_model
+        self.decompsition = series_decomp(configs.moving_avg)
+        self.individual = individual
+        self.channels = configs.enc_in
+        self.enc_in = 1 if configs.CI else configs.enc_in
+        if self.individual:
+            self.Linear_Seasonal = nn.ModuleList()
+            self.Linear_Trend = nn.ModuleList()
+
+            for i in range(self.channels):
+                self.Linear_Seasonal.append(nn.Linear(self.seq_len, self.pred_len))
+                self.Linear_Trend.append(nn.Linear(self.seq_len, self.pred_len))
+
+                self.Linear_Seasonal[i].weight = nn.Parameter(
+                    (1 / self.seq_len) * torch.ones([self.pred_len, self.seq_len])
+                )
+                self.Linear_Trend[i].weight = nn.Parameter(
+                    (1 / self.seq_len) * torch.ones([self.pred_len, self.seq_len])
+                )
+        else:
+            self.Linear_Seasonal = nn.Linear(self.seq_len, self.pred_len)
+            self.Linear_Trend = nn.Linear(self.seq_len, self.pred_len)
+
+            self.Linear_Seasonal.weight = nn.Parameter(
+                (1 / self.seq_len) * torch.ones([self.pred_len, self.seq_len])
+            )
+            self.Linear_Trend.weight = nn.Parameter(
+                (1 / self.seq_len) * torch.ones([self.pred_len, self.seq_len])
+            )
+
+    def encoder(self, x):
+        seasonal_init, trend_init = self.decompsition(x)
+        seasonal_init, trend_init = (
+            seasonal_init.permute(0, 2, 1),
+            trend_init.permute(0, 2, 1),
+        )
+        if self.individual:
+            seasonal_output = torch.zeros(
+                [seasonal_init.size(0), seasonal_init.size(1), self.pred_len],
+                dtype=seasonal_init.dtype,
+            ).to(seasonal_init.device)
+            trend_output = torch.zeros(
+                [trend_init.size(0), trend_init.size(1), self.pred_len],
+                dtype=trend_init.dtype,
+            ).to(trend_init.device)
+            for i in range(self.channels):
+                seasonal_output[:, i, :] = self.Linear_Seasonal[i](
+                    seasonal_init[:, i, :]
+                )
+                trend_output[:, i, :] = self.Linear_Trend[i](trend_init[:, i, :])
+        else:
+            seasonal_output = self.Linear_Seasonal(seasonal_init)
+            trend_output = self.Linear_Trend(trend_init)
+        x = seasonal_output + trend_output
+        return x.permute(0, 2, 1)
+
+    def forecast(self, x_enc):
+        # Encoder
+        return self.encoder(x_enc)
+
+    def forward(self, x_enc):
+        if x_enc.shape[0] == 0:
+            return torch.empty((0, self.pred_len, self.enc_in)).to(x_enc.device)
+        dec_out = self.forecast(x_enc)
+        return dec_out[:, -self.pred_len :, :]  # [B, L, D]
diff --git a/pytorch_forecasting/models/duet/sub_modules/utils/__init__.py b/pytorch_forecasting/models/duet/sub_modules/utils/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/pytorch_forecasting/models/duet/sub_modules/utils/losses.py b/pytorch_forecasting/models/duet/sub_modules/utils/losses.py
new file mode 100644
index 000000000..dc1773746
--- /dev/null
+++ b/pytorch_forecasting/models/duet/sub_modules/utils/losses.py
@@ -0,0 +1,112 @@
+# This source code is provided for the purposes of scientific reproducibility
+# under the following limited license from Element AI Inc. The code is an
+# implementation of the N-BEATS model (Oreshkin et al., N-BEATS: Neural basis
+# expansion analysis for interpretable time data forecasting,
+# https://arxiv.org/abs/1905.10437). The copyright to the source code is
+# licensed under the Creative Commons - Attribution-NonCommercial 4.0
+# International license (CC BY-NC 4.0):
+# https://creativecommons.org/licenses/by-nc/4.0/.  Any commercial use (whether
+# for the benefit of third parties or internally in production) requires an
+# explicit license. The subject-matter of the N-BEATS model and associated
+# materials are the property of Element AI Inc. and may be subject to patent
+# protection. No license to patents is granted hereunder (whether express or
+# implied). Copyright © 2020 Element AI Inc. All rights reserved.
+
+"""
+Loss functions for PyTorch.
+"""
+
+import numpy as np
+import torch as t
+import torch.nn as nn
+
+
+def divide_no_nan(a, b):
+    """
+    a/b where the resulted NaN or Inf are replaced by 0.
+    """
+    result = a / b
+    result[result != result] = 0.0
+    result[result == np.inf] = 0.0
+    return result
+
+
+class mape_loss(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(
+        self,
+        insample: t.Tensor,
+        freq: int,
+        forecast: t.Tensor,
+        target: t.Tensor,
+        mask: t.Tensor,
+    ) -> t.float:
+        """
+        MAPE loss as defined in:
+        https://en.wikipedia.org/wiki/Mean_absolute_percentage_error
+
+        :param forecast: Forecast values. Shape: batch, time
+        :param target: Target values. Shape: batch, time
+        :param mask: 0/1 mask. Shape: batch, time
+        :return: Loss value
+        """
+        weights = divide_no_nan(mask, target)
+        return t.mean(t.abs((forecast - target) * weights))
+
+
+class smape_loss(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(
+        self,
+        insample: t.Tensor,
+        freq: int,
+        forecast: t.Tensor,
+        target: t.Tensor,
+        mask: t.Tensor,
+    ) -> t.float:
+        """
+        sMAPE loss as defined in https://robjhyndman.com/hyndsight/smape/
+        (Makridakis 1993)
+
+        :param forecast: Forecast values. Shape: batch, time
+        :param target: Target values. Shape: batch, time
+        :param mask: 0/1 mask. Shape: batch, time
+        :return: Loss value
+        """
+        return 200 * t.mean(
+            divide_no_nan(
+                t.abs(forecast - target), t.abs(forecast.data) + t.abs(target.data)
+            )
+            * mask
+        )
+
+
+class mase_loss(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(
+        self,
+        insample: t.Tensor,
+        freq: int,
+        forecast: t.Tensor,
+        target: t.Tensor,
+        mask: t.Tensor,
+    ) -> t.float:
+        """
+        MASE loss as defined in "Scaled Errors" https://robjhyndman.com/papers/mase.pdf
+
+        :param insample: Insample values. Shape: batch, time_i
+        :param freq: Frequency value
+        :param forecast: Forecast values. Shape: batch, time_o
+        :param target: Target values. Shape: batch, time_o
+        :param mask: 0/1 mask. Shape: batch, time_o
+        :return: Loss value
+        """
+        masep = t.mean(t.abs(insample[:, freq:] - insample[:, :-freq]), dim=1)
+        masked_masep_inv = divide_no_nan(mask, masep[:, None])
+        return t.mean(t.abs(target - forecast) * masked_masep_inv)
diff --git a/pytorch_forecasting/models/duet/sub_modules/utils/masked_attention.py b/pytorch_forecasting/models/duet/sub_modules/utils/masked_attention.py
new file mode 100644
index 000000000..001d93990
--- /dev/null
+++ b/pytorch_forecasting/models/duet/sub_modules/utils/masked_attention.py
@@ -0,0 +1,213 @@
+import math
+from math import sqrt
+
+from pytorch_forecasting.utils._dependencies._safe_import import _safe_import
+
+rearrange = _safe_import("einops.rearrange")
+import torch
+import torch.fft
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn.functional import gumbel_softmax
+
+
+class EncoderLayer(nn.Module):
+    def __init__(self, attention, d_model, d_ff=None, dropout=0.1, activation="relu"):
+        super().__init__()
+        d_ff = d_ff or 4 * d_model
+        self.attention = attention
+        self.conv1 = nn.Conv1d(in_channels=d_model, out_channels=d_ff, kernel_size=1)
+        self.conv2 = nn.Conv1d(in_channels=d_ff, out_channels=d_model, kernel_size=1)
+        self.norm1 = nn.LayerNorm(d_model)
+        self.norm2 = nn.LayerNorm(d_model)
+        self.dropout = nn.Dropout(dropout)
+        self.activation = F.relu if activation == "relu" else F.gelu
+
+    def forward(self, x, attn_mask=None, tau=None, delta=None):
+        new_x, attn = self.attention(x, x, x, attn_mask=attn_mask, tau=tau, delta=delta)
+        x = x + self.dropout(new_x)
+
+        y = x = self.norm1(x)
+        y = self.dropout(self.activation(self.conv1(y.transpose(-1, 1))))
+        y = self.dropout(self.conv2(y).transpose(-1, 1))
+
+        return self.norm2(x + y), attn
+
+
+class Encoder(nn.Module):
+    def __init__(self, attn_layers, conv_layers=None, norm_layer=None):
+        super().__init__()
+        self.attn_layers = nn.ModuleList(attn_layers)
+        self.conv_layers = (
+            nn.ModuleList(conv_layers) if conv_layers is not None else None
+        )
+        self.norm = norm_layer
+
+    def forward(self, x, attn_mask=None, tau=None, delta=None):
+        # x [B, L, D]
+        attns = []
+        if self.conv_layers is not None:
+            for i, (attn_layer, conv_layer) in enumerate(
+                zip(self.attn_layers, self.conv_layers)
+            ):
+                delta = delta if i == 0 else None
+                x, attn = attn_layer(x, attn_mask=attn_mask, tau=tau, delta=delta)
+                x = conv_layer(x)
+                attns.append(attn)
+            x, attn = self.attn_layers[-1](x, tau=tau, delta=None)
+            attns.append(attn)
+        else:
+            for attn_layer in self.attn_layers:
+                x, attn = attn_layer(x, attn_mask=attn_mask, tau=tau, delta=delta)
+                attns.append(attn)
+
+        if self.norm is not None:
+            x = self.norm(x)
+
+        return x, attns
+
+
+class FullAttention(nn.Module):
+    def __init__(
+        self,
+        mask_flag=True,
+        factor=5,
+        scale=None,
+        attention_dropout=0.1,
+        output_attention=False,
+    ):
+        super().__init__()
+        self.scale = scale
+        self.mask_flag = mask_flag
+        self.output_attention = output_attention
+        self.dropout = nn.Dropout(attention_dropout)
+
+    def forward(self, queries, keys, values, attn_mask, tau=None, delta=None):
+        B, L, H, E = queries.shape
+        _, S, _, D = values.shape
+        scale = self.scale or 1.0 / sqrt(E)
+
+        scores = torch.einsum("blhe,bshe->bhls", queries, keys)
+
+        # if self.mask_flag:
+        #     large_negative = -math.log(1e10)
+        #     attention_mask = torch.where(attn_mask == 0,
+        #                                  torch.tensor(large_negative),
+        #                                  attn_mask
+        #     )
+        #
+        #     scores = scores * attention_mask
+        if self.mask_flag:
+            large_negative = -math.log(1e10)
+            attention_mask = torch.where(attn_mask == 0, large_negative, 0)
+
+            scores = scores * attn_mask + attention_mask
+
+        A = self.dropout(torch.softmax(scale * scores, dim=-1))
+        V = torch.einsum("bhls,bshd->blhd", A, values)
+
+        if self.output_attention:
+            return V.contiguous(), A
+        else:
+            return V.contiguous(), None
+
+
+class AttentionLayer(nn.Module):
+    def __init__(self, attention, d_model, n_heads, d_keys=None, d_values=None):
+        super().__init__()
+
+        d_keys = d_keys or (d_model // n_heads)
+        d_values = d_values or (d_model // n_heads)
+
+        self.inner_attention = attention
+        self.query_projection = nn.Linear(d_model, d_keys * n_heads)
+        self.key_projection = nn.Linear(d_model, d_keys * n_heads)
+        self.value_projection = nn.Linear(d_model, d_values * n_heads)
+        self.out_projection = nn.Linear(d_values * n_heads, d_model)
+        self.n_heads = n_heads
+
+    def forward(self, queries, keys, values, attn_mask, tau=None, delta=None):
+        B, L, _ = queries.shape
+        _, S, _ = keys.shape
+        H = self.n_heads
+
+        queries = self.query_projection(queries).view(B, L, H, -1)
+        keys = self.key_projection(keys).view(B, S, H, -1)
+        values = self.value_projection(values).view(B, S, H, -1)
+
+        out, attn = self.inner_attention(
+            queries, keys, values, attn_mask, tau=tau, delta=delta
+        )
+        out = out.view(B, L, -1)
+
+        return self.out_projection(out), attn
+
+
+class Mahalanobis_mask(nn.Module):
+    def __init__(self, input_size):
+        super().__init__()
+        frequency_size = input_size // 2 + 1
+        self.A = nn.Parameter(
+            torch.randn(frequency_size, frequency_size), requires_grad=True
+        )
+
+    def calculate_prob_distance(self, X):
+        XF = torch.abs(torch.fft.rfft(X, dim=-1))
+        X1 = XF.unsqueeze(2)
+        X2 = XF.unsqueeze(1)
+
+        # B x C x C x D
+        diff = X1 - X2
+
+        temp = torch.einsum("dk,bxck->bxcd", self.A, diff)
+
+        dist = torch.einsum("bxcd,bxcd->bxc", temp, temp)
+
+        # exp_dist = torch.exp(-dist)
+        exp_dist = 1 / (dist + 1e-10)
+        # 对角线置零
+
+        identity_matrices = 1 - torch.eye(exp_dist.shape[-1])
+        mask = identity_matrices.repeat(exp_dist.shape[0], 1, 1).to(exp_dist.device)
+        exp_dist = torch.einsum("bxc,bxc->bxc", exp_dist, mask)
+        exp_max, _ = torch.max(exp_dist, dim=-1, keepdim=True)
+        exp_max = exp_max.detach()
+
+        epsilon = 1e-10
+        # B x C x C
+        p = exp_dist / (exp_max + epsilon)
+
+        identity_matrices = torch.eye(p.shape[-1])
+        p1 = torch.einsum("bxc,bxc->bxc", p, mask)
+
+        diag = identity_matrices.repeat(p.shape[0], 1, 1).to(p.device)
+        p = (p1 + diag) * 0.99
+
+        return p
+
+    def bernoulli_gumbel_rsample(self, distribution_matrix):
+        b, c, d = distribution_matrix.shape
+
+        flatten_matrix = rearrange(distribution_matrix, "b c d -> (b c d) 1")
+        r_flatten_matrix = 1 - flatten_matrix
+
+        log_flatten_matrix = torch.log(flatten_matrix / r_flatten_matrix)
+        log_r_flatten_matrix = torch.log(r_flatten_matrix / flatten_matrix)
+
+        new_matrix = torch.concat([log_flatten_matrix, log_r_flatten_matrix], dim=-1)
+        resample_matrix = gumbel_softmax(new_matrix, hard=True)
+
+        resample_matrix = rearrange(
+            resample_matrix[..., 0], "(b c d) -> b c d", b=b, c=c, d=d
+        )
+        return resample_matrix
+
+    def forward(self, X):
+        p = self.calculate_prob_distance(X)
+
+        # bernoulli中两个通道有关系的概率
+        sample = self.bernoulli_gumbel_rsample(p)
+
+        mask = sample.unsqueeze(1)
+        cnt = torch.sum(mask, dim=-1)  # noqa: F841
+        return mask
diff --git a/pytorch_forecasting/models/duet/sub_modules/utils/masking.py b/pytorch_forecasting/models/duet/sub_modules/utils/masking.py
new file mode 100644
index 000000000..6ef13efab
--- /dev/null
+++ b/pytorch_forecasting/models/duet/sub_modules/utils/masking.py
@@ -0,0 +1,28 @@
+import torch
+
+
+class TriangularCausalMask:
+    def __init__(self, B, L, device="cpu"):
+        mask_shape = [B, 1, L, L]
+        with torch.no_grad():
+            self._mask = torch.triu(
+                torch.ones(mask_shape, dtype=torch.bool), diagonal=1
+            ).to(device)
+
+    @property
+    def mask(self):
+        return self._mask
+
+
+class ProbMask:
+    def __init__(self, B, H, L, index, scores, device="cpu"):
+        _mask = torch.ones(L, scores.shape[-1], dtype=torch.bool).to(device).triu(1)
+        _mask_ex = _mask[None, None, :].expand(B, H, L, scores.shape[-1])
+        indicator = _mask_ex[
+            torch.arange(B)[:, None, None], torch.arange(H)[None, :, None], index, :
+        ].to(device)
+        self._mask = indicator.view(scores.shape).to(device)
+
+    @property
+    def mask(self):
+        return self._mask
diff --git a/pytorch_forecasting/models/duet/sub_modules/utils/timefeatures.py b/pytorch_forecasting/models/duet/sub_modules/utils/timefeatures.py
new file mode 100644
index 000000000..2dbc9c669
--- /dev/null
+++ b/pytorch_forecasting/models/duet/sub_modules/utils/timefeatures.py
@@ -0,0 +1,150 @@
+# From: gluonts/src/gluonts/time_feature/_base.py
+# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License").
+# You may not use this file except in compliance with the License.
+# A copy of the License is located at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# or in the "license" file accompanying this file. This file is distributed
+# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
+# express or implied. See the License for the specific language governing
+# permissions and limitations under the License.
+
+from typing import List  # noqa: UP035
+
+import numpy as np
+import pandas as pd
+from pandas.tseries import offsets
+from pandas.tseries.frequencies import to_offset
+
+
+class TimeFeature:
+    def __init__(self):
+        pass
+
+    def __call__(self, index: pd.DatetimeIndex) -> np.ndarray:
+        pass
+
+    def __repr__(self):
+        return self.__class__.__name__ + "()"
+
+
+class SecondOfMinute(TimeFeature):
+    """Minute of hour encoded as value between [-0.5, 0.5]"""
+
+    def __call__(self, index: pd.DatetimeIndex) -> np.ndarray:
+        return index.second / 59.0 - 0.5
+
+
+class MinuteOfHour(TimeFeature):
+    """Minute of hour encoded as value between [-0.5, 0.5]"""
+
+    def __call__(self, index: pd.DatetimeIndex) -> np.ndarray:
+        return index.minute / 59.0 - 0.5
+
+
+class HourOfDay(TimeFeature):
+    """Hour of day encoded as value between [-0.5, 0.5]"""
+
+    def __call__(self, index: pd.DatetimeIndex) -> np.ndarray:
+        return index.hour / 23.0 - 0.5
+
+
+class DayOfWeek(TimeFeature):
+    """Hour of day encoded as value between [-0.5, 0.5]"""
+
+    def __call__(self, index: pd.DatetimeIndex) -> np.ndarray:
+        return index.dayofweek / 6.0 - 0.5
+
+
+class DayOfMonth(TimeFeature):
+    """Day of month encoded as value between [-0.5, 0.5]"""
+
+    def __call__(self, index: pd.DatetimeIndex) -> np.ndarray:
+        return (index.day - 1) / 30.0 - 0.5
+
+
+class DayOfYear(TimeFeature):
+    """Day of year encoded as value between [-0.5, 0.5]"""
+
+    def __call__(self, index: pd.DatetimeIndex) -> np.ndarray:
+        return (index.dayofyear - 1) / 365.0 - 0.5
+
+
+class MonthOfYear(TimeFeature):
+    """Month of year encoded as value between [-0.5, 0.5]"""
+
+    def __call__(self, index: pd.DatetimeIndex) -> np.ndarray:
+        return (index.month - 1) / 11.0 - 0.5
+
+
+class WeekOfYear(TimeFeature):
+    """Week of year encoded as value between [-0.5, 0.5]"""
+
+    def __call__(self, index: pd.DatetimeIndex) -> np.ndarray:
+        return (index.isocalendar().week - 1) / 52.0 - 0.5
+
+
+def time_features_from_frequency_str(freq_str: str) -> list[TimeFeature]:
+    """
+    Returns a list of time features that will be appropriate for the given frequency
+    string.
+    Parameters
+    ----------
+    freq_str
+        Frequency string of the form [multiple][granularity] such as "12H",
+        "5min", "1D" etc.
+    """
+
+    features_by_offsets = {
+        offsets.YearEnd: [],
+        offsets.QuarterEnd: [MonthOfYear],
+        offsets.MonthEnd: [MonthOfYear],
+        offsets.Week: [DayOfMonth, WeekOfYear],
+        offsets.Day: [DayOfWeek, DayOfMonth, DayOfYear],
+        offsets.BusinessDay: [DayOfWeek, DayOfMonth, DayOfYear],
+        offsets.Hour: [HourOfDay, DayOfWeek, DayOfMonth, DayOfYear],
+        offsets.Minute: [
+            MinuteOfHour,
+            HourOfDay,
+            DayOfWeek,
+            DayOfMonth,
+            DayOfYear,
+        ],
+        offsets.Second: [
+            SecondOfMinute,
+            MinuteOfHour,
+            HourOfDay,
+            DayOfWeek,
+            DayOfMonth,
+            DayOfYear,
+        ],
+    }
+
+    offset = to_offset(freq_str)
+
+    for offset_type, feature_classes in features_by_offsets.items():
+        if isinstance(offset, offset_type):
+            return [cls() for cls in feature_classes]
+
+    supported_freq_msg = f"""
+    Unsupported frequency {freq_str}
+    The following frequencies are supported:
+        Y   - yearly
+            alias: A
+        M   - monthly
+        W   - weekly
+        D   - daily
+        B   - business days
+        H   - hourly
+        T   - minutely
+            alias: min
+        S   - secondly
+    """
+    raise RuntimeError(supported_freq_msg)
+
+
+def time_features(dates, freq="h"):
+    return np.vstack([feat(dates) for feat in time_features_from_frequency_str(freq)])
diff --git a/pytorch_forecasting/models/duet/sub_modules/utils/tools.py b/pytorch_forecasting/models/duet/sub_modules/utils/tools.py
new file mode 100644
index 000000000..d8e0d69f2
--- /dev/null
+++ b/pytorch_forecasting/models/duet/sub_modules/utils/tools.py
@@ -0,0 +1,124 @@
+import copy
+import os
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import torch
+
+plt.switch_backend("agg")
+
+
+def adjust_learning_rate(optimizer, epoch, args):
+    # lr = args.learning_rate * (0.2 ** (epoch // 2))
+    if args.lradj == "type1":
+        lr_adjust = {epoch: args.lr * (0.5 ** ((epoch - 1) // 1))}
+    elif args.lradj == "type2":
+        lr_adjust = {2: 5e-5, 4: 1e-5, 6: 5e-6, 8: 1e-6, 10: 5e-7, 15: 1e-7, 20: 5e-8}
+
+    elif args.lradj == "type3":
+        lr_adjust = {
+            epoch: args.lr if epoch < 3 else args.lr * (0.9 ** ((epoch - 3) // 1))
+        }
+    elif args.lradj == "constant":
+        lr_adjust = {epoch: args.lr}
+
+    if epoch in lr_adjust.keys():
+        lr = lr_adjust[epoch]
+        for param_group in optimizer.param_groups:
+            param_group["lr"] = lr
+        print(f"Updating learning rate to {lr}")
+
+
+class EarlyStopping:
+    def __init__(self, patience=7, delta=0):
+        self.patience = patience
+        self.counter = 0
+        self.best_score = None
+        self.early_stop = False
+        self.val_loss_min = np.Inf
+        self.delta = delta
+        self.check_point = None
+
+    def __call__(self, val_loss, model):
+        score = -val_loss
+        if self.best_score is None:
+            self.best_score = score
+            self.save_checkpoint(val_loss, model)
+        elif score < self.best_score + self.delta:
+            self.counter += 1
+            print(f"EarlyStopping counter: {self.counter} out of {self.patience}")
+            if self.counter >= self.patience:
+                self.early_stop = True
+        else:
+            self.best_score = score
+            self.save_checkpoint(val_loss, model)
+            self.counter = 0
+
+    def save_checkpoint(self, val_loss, model):
+        print(
+            f"Validation loss decreased ({self.val_loss_min:.6f} --> {val_loss:.6f}). Saving model ..."  # noqa: E501
+        )
+        self.check_point = copy.deepcopy(model.state_dict())
+        # self.check_point = model.state_dict()
+        self.val_loss_min = val_loss
+
+
+class dotdict(dict):
+    """dot.notation access to dictionary attributes"""
+
+    __getattr__ = dict.get
+    __setattr__ = dict.__setitem__
+    __delattr__ = dict.__delitem__
+
+
+class StandardScaler:
+    def __init__(self, mean, std):
+        self.mean = mean
+        self.std = std
+
+    def transform(self, data):
+        return (data - self.mean) / self.std
+
+    def inverse_transform(self, data):
+        return (data * self.std) + self.mean
+
+
+def visual(true, preds=None, name="./pic/test.pdf"):
+    """
+    Results visualization
+    """
+    plt.figure()
+    plt.plot(true, label="GroundTruth", linewidth=2)
+    if preds is not None:
+        plt.plot(preds, label="Prediction", linewidth=2)
+    plt.legend()
+    plt.savefig(name, bbox_inches="tight")
+
+
+def adjustment(gt, pred):
+    anomaly_state = False
+    for i in range(len(gt)):
+        if gt[i] == 1 and pred[i] == 1 and not anomaly_state:
+            anomaly_state = True
+            for j in range(i, 0, -1):
+                if gt[j] == 0:
+                    break
+                else:
+                    if pred[j] == 0:
+                        pred[j] = 1
+            for j in range(i, len(gt)):
+                if gt[j] == 0:
+                    break
+                else:
+                    if pred[j] == 0:
+                        pred[j] = 1
+        elif gt[i] == 0:
+            anomaly_state = False
+        if anomaly_state:
+            pred[i] = 1
+    return gt, pred
+
+
+def cal_accuracy(y_pred, y_true):
+    return np.mean(y_pred == y_true)
diff --git a/tests/test_models/test_duet.py b/tests/test_models/test_duet.py
new file mode 100644
index 000000000..97bc5ec29
--- /dev/null
+++ b/tests/test_models/test_duet.py
@@ -0,0 +1,132 @@
+import lightning.pytorch as pl
+from lightning.pytorch.callbacks import Callback
+from lightning.pytorch.tuner import Tuner
+import numpy as np
+import pandas as pd
+
+from pytorch_forecasting.data import TimeSeriesDataSet
+from pytorch_forecasting.data.encoders import GroupNormalizer
+from pytorch_forecasting.metrics import MAE, MAPE, SMAPE
+from pytorch_forecasting.models.duet.duet import DUETModel
+
+
+class ModelSummary(Callback):
+    def on_validation_epoch_end(self, trainer, pl_module):
+        """Log metrics at the end of each validation epoch."""
+        metrics = trainer.callback_metrics
+
+        print(f"\n--- Epoch {trainer.current_epoch} Summary ---")
+        if metrics:
+            for key, value in metrics.items():
+                print(f"{key}: {value.item():.4f}")
+        else:
+            print("No metrics logged for this epoch.")
+        print("--------------------------------------\n")
+
+
+def configure_dataset() -> tuple[TimeSeriesDataSet, TimeSeriesDataSet]:
+    df = pd.read_csv("pytorch_forecasting/models/duet/dataset/AQWan.csv")
+
+    df.drop("name", axis=1, inplace=True)
+    df["date"] = pd.to_datetime(df["date"])
+    df.sort_values(by=["cols", "date"], inplace=True)
+    df["data"] = df.groupby("cols")["data"].transform(lambda x: x.ffill())
+    df["data"] = df.groupby("cols")["data"].transform(lambda x: x.bfill())
+
+    # Creating time_idx column
+    df["time_idx"] = df.groupby("cols").cumcount()
+
+    print("------------------Data Properties------------------")
+    print(df.info())
+    print("---------------------------------------------------")
+
+    max_time_idx = df["time_idx"].max()
+
+    # Training on 80% of data
+    training_cutoff = int(max_time_idx * 0.8)
+
+    # Create the training dataset
+    training_dataset = TimeSeriesDataSet(
+        df[lambda x: x.time_idx <= training_cutoff],
+        time_idx="time_idx",
+        target="data",
+        group_ids=["cols"],
+        max_encoder_length=512,
+        max_prediction_length=96,
+        target_normalizer=GroupNormalizer(groups=["cols"]),
+        static_categoricals=["cols"],
+        time_varying_unknown_reals=["data"],
+    )
+
+    validation_dataset = TimeSeriesDataSet.from_dataset(
+        training_dataset, df, min_prediction_idx=training_cutoff + 1
+    )
+
+    return training_dataset, validation_dataset
+
+
+def test_duet_from_dataset():
+    trainset, validationset = configure_dataset()
+    assert trainset is not None
+    assert validationset is not None
+
+    train_dataloader = trainset.to_dataloader(train=True, batch_size=32, num_workers=0)
+    val_dataloader = validationset.to_dataloader(
+        train=False, batch_size=32, num_workers=0
+    )
+
+    # {"CI": 1, "batch_size": 32, "d_ff": 128, "d_model": 128, "dropout": 0.3,
+    # "e_layers": 1, "factor": 3, "fc_dropout": 0.1, "horizon": 96, "k": 1,
+    # "loss": "MAE", "lr": 0.0005, "lradj": "type1", "n_heads": 1, "norm": true,
+    # "num_epochs": 100, "num_experts": 3, "patch_len": 48, "patience": 5,
+    # "seq_len": 512}
+
+    model = DUETModel.from_dataset(
+        trainset,
+        CI=1,
+        d_ff=128,
+        d_model=128,
+        dropout=0.3,
+        e_layers=1,
+        factor=3,
+        fc_dropout=0.1,
+        k=1,
+        lradj="type1",
+        n_heads=1,
+        norm=True,
+        num_experts=3,
+        patch_len=48,
+        patience=5,
+        learning_rate=5e-4,
+        loss=MAE(),
+    )
+
+    trainer = pl.Trainer(
+        max_epochs=4,
+        accelerator="auto",
+        gradient_clip_val=0.1,
+        limit_train_batches=50,  # for faster testing
+        limit_val_batches=50,  # for faster testing
+        fast_dev_run=True,  # for faster testing
+        callbacks=[ModelSummary()],
+    )
+
+    # Find optimal learning rate
+    Tuner(trainer).lr_find(
+        model,
+        train_dataloaders=train_dataloader,
+        val_dataloaders=val_dataloader,
+        min_lr=1e-10,
+        max_lr=1e-1,
+    )
+
+    # Start the training
+    trainer.fit(
+        model,
+        train_dataloaders=train_dataloader,
+        val_dataloaders=val_dataloader,
+    )
+
+
+if __name__ == "__main__":
+    test_duet_from_dataset()