time series forecasting model (#17965)

* initial files * initial model via cli * typos * make a start on the model config * ready with configuation * remove tokenizer ref. * init the transformer * added initial model forward to return dec_output * require gluonts * update dep. ver table and add as extra * fixed typo * add type for prediction_length * use num_time_features * use config * more config * typos * opps another typo * freq can be none * default via transformation is 1 * initial transformations * fix imports * added transform_start_field * add helper to create pytorch dataloader * added inital val and test data loader * added initial distr head and loss * training working * remove TimeSeriesTransformerTokenizer Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * Update src/transformers/__init__.py Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * Update src/transformers/models/time_series_transformer/__init__.py Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * fixed copyright * removed docs * remove time series tokenizer * fixed docs * fix text * fix second * fix default * fix order * use config directly * undo change * fix comment * fix year * fix import * add additional arguments for training vs. test * initial greedy inference loop * fix inference * comment out token inputs to enc dec * Use HF encoder/decoder * fix inference * Use Seq2SeqTSModelOutput output * return Seq2SeqTSPredictionOutput * added default arguments * fix return_dict true * scale is a tensor * output static_features for inference * clean up some unused bits * fixed typo * set return_dict if none * call model once for both train/predict * use cache if future_target is none * initial generate func * generate arguments * future_time_feat is required * return SampleTSPredictionOutput * removed unneeded classes * fix when params is none * fix return dict * fix num_attention_heads * fix arguments * remove unused shift_tokens_right * add different dropout configs * implement FeatureEmbedder, Scaler and weighted_average * remove gluonts dependency * fix class names * avoid _variable names * remove gluonts dependency * fix imports * remove gluonts from configuration * fix docs * fixed typo * move utils to examples * add example requirements * config has no freq * initial run_ts_no_trainer * remove from ignore * fix output_attentions and removed unsued getters/setters * removed unsed tests * add dec seq len * add test_attention_outputs * set has_text_modality=False * add config attribute_map * make style * make fix-copies * add encoder_outputs to TimeSeriesTransformerForPrediction forward * Improve docs, add model to README * added test_forward_signature * More improvements * Add more copied from * Fix README * Fix remaining quality issues * updated encoder and decoder * fix generate * output_hidden_states and use_cache are optional * past key_values returned too * initialize weights of distribution_output module * fixed more tests * update test_forward_signature * fix return_dict outputs * Update src/transformers/models/time_series_transformer/configuration_time_series_transformer.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/time_series_transformer/configuration_time_series_transformer.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/time_series_transformer/configuration_time_series_transformer.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/time_series_transformer/configuration_time_series_transformer.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/time_series_transformer/modeling_time_series_transformer.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/time_series_transformer/modeling_time_series_transformer.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/time_series_transformer/modeling_time_series_transformer.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * removed commented out tests * added neg. bin and normal output * Update src/transformers/models/time_series_transformer/configuration_time_series_transformer.py Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * move to one line * Add docstrings * Update src/transformers/models/time_series_transformer/configuration_time_series_transformer.py Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * add try except for assert and raise * try and raise exception * fix the documentation formatting * fix assert call * fix docstring formatting * removed input_ids from DOCSTRING * Update input docstring * Improve variable names * Update order of inputs * Improve configuration * Improve variable names * Improve docs * Remove key_length from tests * Add extra docs * initial unittests * added test_inference_no_head test * added test_inference_head * add test_seq_to_seq_generation * make style * one line * assert mean prediction * removed comments * Update src/transformers/models/time_series_transformer/modeling_time_series_transformer.py Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * Update src/transformers/models/time_series_transformer/modeling_time_series_transformer.py Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * fix order of args * make past_observed_mask optional as well * added Amazon license header * updated utils with new fieldnames * make style * cleanup * undo position of past_observed_mask * fix import * typo * more typo * rename example files * remove example for now * Update docs/source/en/_toctree.yml Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/time_series_transformer/configuration_time_series_transformer.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/time_series_transformer/modeling_time_series_transformer.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/time_series_transformer/modeling_time_series_transformer.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update modeling_time_series_transformer.py fix style * fixed typo * fix typo and grammer * fix style Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> Co-authored-by: NielsRogge <niels.rogge1@gmail.com> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
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diff --git a/README.md b/README.md
@@ -375,6 +375,7 @@ Current number of checkpoints: ![](https://img.shields.io/endpoint?url=https://h
 1. **[T5v1.1](https://huggingface.co/docs/transformers/model_doc/t5v1.1)** (from Google AI) released in the repository [google-research/text-to-text-transfer-transformer](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#t511) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
 1. **[TAPAS](https://huggingface.co/docs/transformers/model_doc/tapas)** (from Google AI) released with the paper [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://arxiv.org/abs/2004.02349) by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno and Julian Martin Eisenschlos.
 1. **[TAPEX](https://huggingface.co/docs/transformers/model_doc/tapex)** (from Microsoft Research) released with the paper [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou.
+1. **[Time Series Transformer](https://huggingface.co/docs/transformers/main/model_doc/time_series_transformer)**  (from HuggingFace).
 1. **[Trajectory Transformer](https://huggingface.co/docs/transformers/model_doc/trajectory_transformers)** (from the University of California at Berkeley) released with the paper [Offline Reinforcement Learning as One Big Sequence Modeling Problem](https://arxiv.org/abs/2106.02039) by Michael Janner, Qiyang Li, Sergey Levine
 1. **[Transformer-XL](https://huggingface.co/docs/transformers/model_doc/transfo-xl)** (from Google/CMU) released with the paper [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://arxiv.org/abs/1901.02860) by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
 1. **[TrOCR](https://huggingface.co/docs/transformers/model_doc/trocr)** (from Microsoft), released together with the paper [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang, Zhoujun Li, Furu Wei.

diff --git a/README_ko.md b/README_ko.md
@@ -325,6 +325,7 @@ Flax, PyTorch, TensorFlow 설치 페이지에서 이들을 conda로 설치하는
 1. **[T5v1.1](https://huggingface.co/docs/transformers/model_doc/t5v1.1)** (from Google AI) released in the repository [google-research/text-to-text-transfer-transformer](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#t511) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
 1. **[TAPAS](https://huggingface.co/docs/transformers/model_doc/tapas)** (from Google AI) released with the paper [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://arxiv.org/abs/2004.02349) by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno and Julian Martin Eisenschlos.
 1. **[TAPEX](https://huggingface.co/docs/transformers/model_doc/tapex)** (from Microsoft Research) released with the paper [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou.
+1. **[Time Series Transformer](https://huggingface.co/docs/transformers/main/model_doc/time_series_transformer)** (from HuggingFace). 
 1. **[Trajectory Transformer](https://huggingface.co/docs/transformers/model_doc/trajectory_transformers)** (from the University of California at Berkeley) released with the paper [Offline Reinforcement Learning as One Big Sequence Modeling Problem](https://arxiv.org/abs/2106.02039) by Michael Janner, Qiyang Li, Sergey Levine 
 1. **[Transformer-XL](https://huggingface.co/docs/transformers/model_doc/transfo-xl)** (from Google/CMU) released with the paper [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://arxiv.org/abs/1901.02860) by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
 1. **[TrOCR](https://huggingface.co/docs/transformers/model_doc/trocr)** (from Microsoft), released together with the paper [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang, Zhoujun Li, Furu Wei.

diff --git a/README_zh-hans.md b/README_zh-hans.md
@@ -349,6 +349,7 @@ conda install -c huggingface transformers
 1. **[T5v1.1](https://huggingface.co/docs/transformers/model_doc/t5v1.1)** (来自 Google AI) 伴随论文 [google-research/text-to-text-transfer-transformer](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#t511) 由 Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu 发布。
 1. **[TAPAS](https://huggingface.co/docs/transformers/model_doc/tapas)** (来自 Google AI) 伴随论文 [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://arxiv.org/abs/2004.02349) 由 Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno and Julian Martin Eisenschlos 发布。
 1. **[TAPEX](https://huggingface.co/docs/transformers/model_doc/tapex)** (来自 Microsoft Research) 伴随论文 [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) 由 Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou 发布。
+1. **[Time Series Transformer](https://huggingface.co/docs/transformers/main/model_doc/time_series_transformer)** (from HuggingFace). 
 1. **[Trajectory Transformer](https://huggingface.co/docs/transformers/model_doc/trajectory_transformers)** (from the University of California at Berkeley) released with the paper [Offline Reinforcement Learning as One Big Sequence Modeling Problem](https://arxiv.org/abs/2106.02039) by Michael Janner, Qiyang Li, Sergey Levine 
 1. **[Transformer-XL](https://huggingface.co/docs/transformers/model_doc/transfo-xl)** (来自 Google/CMU) 伴随论文 [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://arxiv.org/abs/1901.02860) 由 Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov 发布。
 1. **[TrOCR](https://huggingface.co/docs/transformers/model_doc/trocr)** (来自 Microsoft) 伴随论文 [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) 由 Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang, Zhoujun Li, Furu Wei 发布。

diff --git a/README_zh-hant.md b/README_zh-hant.md
@@ -361,6 +361,7 @@ conda install -c huggingface transformers
 1. **[T5v1.1](https://huggingface.co/docs/transformers/model_doc/t5v1.1)** (from Google AI) released with the paper [google-research/text-to-text-transfer-transformer](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#t511) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
 1. **[TAPAS](https://huggingface.co/docs/transformers/model_doc/tapas)** (from Google AI) released with the paper [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://arxiv.org/abs/2004.02349) by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno and Julian Martin Eisenschlos.
 1. **[TAPEX](https://huggingface.co/docs/transformers/model_doc/tapex)** (from Microsoft Research) released with the paper [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou.
+1. **[Time Series Transformer](https://huggingface.co/docs/transformers/main/model_doc/time_series_transformer)** (from HuggingFace). 
 1. **[Trajectory Transformer](https://huggingface.co/docs/transformers/model_doc/trajectory_transformers)** (from the University of California at Berkeley) released with the paper [Offline Reinforcement Learning as One Big Sequence Modeling Problem](https://arxiv.org/abs/2106.02039) by Michael Janner, Qiyang Li, Sergey Levine 
 1. **[Transformer-XL](https://huggingface.co/docs/transformers/model_doc/transfo-xl)** (from Google/CMU) released with the paper [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://arxiv.org/abs/1901.02860) by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
 1. **[TrOCR](https://huggingface.co/docs/transformers/model_doc/trocr)** (from Microsoft) released with the paper [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang, Zhoujun Li, Furu Wei.

diff --git a/docs/source/en/_toctree.yml b/docs/source/en/_toctree.yml
@@ -498,6 +498,11 @@
       - local: model_doc/trajectory_transformer
         title: Trajectory Transformer
       title: Reinforcement learning models
+    - isExpanded: false
+      sections:
+      - local: model_doc/time_series_transformer
+        title: Time Series Transformer
+      title: Time series models
     title: Models
   - sections:
     - local: internal/modeling_utils

diff --git a/docs/source/en/index.mdx b/docs/source/en/index.mdx
@@ -165,6 +165,7 @@ The documentation is organized into five sections:
 1. **[T5v1.1](model_doc/t5v1.1)** (from Google AI) released in the repository [google-research/text-to-text-transfer-transformer](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#t511) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
 1. **[TAPAS](model_doc/tapas)** (from Google AI) released with the paper [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://arxiv.org/abs/2004.02349) by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno and Julian Martin Eisenschlos.
 1. **[TAPEX](model_doc/tapex)** (from Microsoft Research) released with the paper [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou.
+1. **[Time Series Transformer](model_doc/time_series_transformer)**  (from HuggingFace).
 1. **[Trajectory Transformer](model_doc/trajectory_transformers)** (from the University of California at Berkeley) released with the paper [Offline Reinforcement Learning as One Big Sequence Modeling Problem](https://arxiv.org/abs/2106.02039) by Michael Janner, Qiyang Li, Sergey Levine
 1. **[Transformer-XL](model_doc/transfo-xl)** (from Google/CMU) released with the paper [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://arxiv.org/abs/1901.02860) by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
 1. **[TrOCR](model_doc/trocr)** (from Microsoft), released together with the paper [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang, Zhoujun Li, Furu Wei.
@@ -310,6 +311,7 @@ Flax), PyTorch, and/or TensorFlow.
 |     Swin Transformer V2     |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |             T5              |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
 |            TAPAS            |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
+|   Time Series Transformer   |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |   Trajectory Transformer    |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |       Transformer-XL        |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
 |            TrOCR            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |

diff --git a/docs/source/en/model_doc/time_series_transformer.mdx b/docs/source/en/model_doc/time_series_transformer.mdx
@@ -0,0 +1,73 @@
+<!--Copyright 2022 The HuggingFace Team. 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. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License 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.
+-->
+
+# Time Series Transformer
+
+<Tip>
+
+This is a recently introduced model so the API hasn't been tested extensively. There may be some bugs or slight
+breaking changes to fix it in the future. If you see something strange, file a [Github Issue](https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title).
+
+</Tip>
+
+## Overview
+
+The Time Series Transformer model is a vanilla encoder-decoder Transformer for time series forecasting.
+
+Tips:
+
+- Similar to other models in the library, [`TimeSeriesTransformerModel`] is the raw Transformer without any head on top, and [`TimeSeriesTransformerForPrediction`]
+adds a distribution head on top of the former, which can be used for time-series forecasting. Note that this is a so-called probabilistic forecasting model, not a
+point forecasting model. This means that the model learns a distribution, from which one can sample. The model doesn't directly output values.
+- [`TimeSeriesTransformerForPrediction`] consists of 2 blocks: an encoder, which takes a `context_length` of time series values as input (called `past_values`),
+and a decoder, which predicts a `prediction_length` of time series values into the future (called `future_values`). During training, one needs to provide
+pairs of (`past_values` and `future_values`) to the model.
+- In addition to the raw (`past_values` and `future_values`), one typically provides additional features to the model. These can be the following:
+    - `past_time_features`: temporal features which the model will add to `past_values`. These serve as "positional encodings" for the Transformer encoder.
+    Examples are "day of the month", "month of the year", etc. as scalar values (and then stacked together as a vector).
+    e.g. if a given time-series value was obtained on the 11th of August, then one could have [11, 8] as time feature vector (11 being "day of the month", 8 being "month of the year").
+    - `future_time_features`: temporal features which the model will add to `future_values`. These serve as "positional encodings" for the Transformer decoder.
+    Examples are "day of the month", "month of the year", etc. as scalar values (and then stacked together as a vector).
+    e.g. if a given time-series value was obtained on the 11th of August, then one could have [11, 8] as time feature vector (11 being "day of the month", 8 being "month of the year").
+    - `static_categorical_features`: categorical features which are static over time (i.e., have the same value for all `past_values` and `future_values`).
+    An example here is the store ID or region ID that identifies a given time-series.
+    Note that these features need to be known for ALL data points (also those in the future).
+    - `static_real_features`: real-valued features which are static over time (i.e., have the same value for all `past_values` and `future_values`).
+    An example here is the image representation of the product for which you have the time-series values (like the [ResNet](resnet) embedding of a "shoe" picture,
+    if your time-series is about the sales of shoes).
+    Note that these features need to be known for ALL data points (also those in the future).
+- The model is trained using "teacher-forcing", similar to how a Transformer is trained for machine translation. This means that, during training, one shifts the
+`future_values` one position to the right as input to the decoder, prepended by the last value of `past_values`. At each time step, the model needs to predict the
+next target. So the set-up of training is similar to a GPT model for language, except that there's no notion of `decoder_start_token_id` (we just use the last value
+of the context as initial input for the decoder).
+- At inference time, we give the final value of the `past_values` as input to the decoder. Next, we can sample from the model to make a prediction at the next time step,
+which is then fed to the decoder in order to make the next prediction (also called autoregressive generation).
+
+
+This model was contributed by [kashif](<https://huggingface.co/kashif).
+
+
+## TimeSeriesTransformerConfig
+
+[[autodoc]] TimeSeriesTransformerConfig
+
+
+## TimeSeriesTransformerModel
+
+[[autodoc]] TimeSeriesTransformerModel
+    - forward
+
+
+## TimeSeriesTransformerForPrediction
+
+[[autodoc]] TimeSeriesTransformerForPrediction
+    - forward
diff --git a/setup.py b/setup.py
@@ -281,6 +281,7 @@ def run(self):
 extras["timm"] = deps_list("timm")
 extras["codecarbon"] = deps_list("codecarbon")
 
+
 extras["sentencepiece"] = deps_list("sentencepiece", "protobuf")
 extras["testing"] = (
     deps_list(

diff --git a/src/transformers/__init__.py b/src/transformers/__init__.py
@@ -336,6 +336,10 @@
     "models.t5": ["T5_PRETRAINED_CONFIG_ARCHIVE_MAP", "T5Config"],
     "models.tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig", "TapasTokenizer"],
     "models.tapex": ["TapexTokenizer"],
+    "models.time_series_transformer": [
+        "TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
+        "TimeSeriesTransformerConfig",
+    ],
     "models.trajectory_transformer": [
         "TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
         "TrajectoryTransformerConfig",
@@ -815,6 +819,15 @@
     _import_structure["modeling_utils"] = ["PreTrainedModel"]
 
     # PyTorch models structure
+
+    _import_structure["models.time_series_transformer"].extend(
+        [
+            "TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
+            "TimeSeriesTransformerForPrediction",
+            "TimeSeriesTransformerModel",
+            "TimeSeriesTransformerPreTrainedModel",
+        ]
+    )
     _import_structure["models.albert"].extend(
         [
             "ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
@@ -3286,6 +3299,10 @@
     from .models.t5 import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, T5Config
     from .models.tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig, TapasTokenizer
     from .models.tapex import TapexTokenizer
+    from .models.time_series_transformer import (
+        TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
+        TimeSeriesTransformerConfig,
+    )
     from .models.trajectory_transformer import (
         TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
         TrajectoryTransformerConfig,
@@ -4577,6 +4594,12 @@
             T5PreTrainedModel,
             load_tf_weights_in_t5,
         )
+        from .models.time_series_transformer import (
+            TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
+            TimeSeriesTransformerForPrediction,
+            TimeSeriesTransformerModel,
+            TimeSeriesTransformerPreTrainedModel,
+        )
         from .models.trajectory_transformer import (
             TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
             TrajectoryTransformerModel,

diff --git a/src/transformers/models/__init__.py b/src/transformers/models/__init__.py
@@ -139,6 +139,7 @@
     t5,
     tapas,
     tapex,
+    time_series_transformer,
     trajectory_transformer,
     transfo_xl,
     trocr,