# Time-Series-Library **Repository Path**: tutu96177/Time-Series-Library ## Basic Information - **Project Name**: Time-Series-Library - **Description**: No description available - **Primary Language**: Unknown - **License**: MIT - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2026-01-22 - **Last Updated**: 2026-03-16 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # Time Series Library (TSLib) TSLib is an open-source library for deep learning researchers, especially for deep time series analysis. > **中文文档**：[README_zh.md](./README_zh.md) We provide a neat code base to evaluate advanced deep time series models or develop your model, which covers five mainstream tasks: **long- and short-term forecasting, imputation, anomaly detection, and classification.** :triangular_flag_on_post:**News** (2025.12) Many thanks to the great work from [ailuntz](https://github.com/thuml/Time-Series-Library/pull/805), which provides an updated requirements and docker deployment, as well as a well-organized document. This is quite meaningful to this project and beginners. :triangular_flag_on_post:**News** (2025.11) Considering the rapid development of Large Time Series Models (LTSMs), we have newly added a [[zero-shot forecasting]](https://github.com/thuml/Time-Series-Library/blob/main/exp/exp_zero_shot_forecasting.py) feature in TSLib. You can try [this script](https://github.com/thuml/Time-Series-Library/blob/main/scripts/long_term_forecast/ETT_script/LTSM.sh) to evaluate LTSMs. :triangular_flag_on_post:**News** (2025.10) Given the recent confusion among researchers regarding minor improvements on standard benchmarks, we propose the [[Accuracy Law]](https://arxiv.org/abs/2510.02729) to characterize the objectives of deep time series forecasting tasks, which can be used to identify saturated datasets. :triangular_flag_on_post:**News** (2024.10) We have included [[TimeXer]](https://arxiv.org/abs/2402.19072), which defined a practical forecasting paradigm: Forecasting with Exogenous Variables. Considering both practicability and computation efficiency, we believe the new forecasting paradigm defined in TimeXer can be the "right" task for future research. :triangular_flag_on_post:**News** (2024.10) Our lab has open-sourced [[OpenLTM]](https://github.com/thuml/OpenLTM), which provides a distinct pretrain-finetuning paradigm compared to TSLib. If you are interested in Large Time Series Models, you may find this repository helpful. :triangular_flag_on_post:**News** (2024.07) We wrote a comprehensive survey of [[Deep Time Series Models]](https://arxiv.org/abs/2407.13278) with a rigorous benchmark based on TSLib. In this paper, we summarized the design principles of current time series models supported by insightful experiments, hoping to be helpful to future research. :triangular_flag_on_post:**News** (2024.04) Many thanks for the great work from [frecklebars](https://github.com/thuml/Time-Series-Library/pull/378). The famous sequential model [Mamba](https://arxiv.org/abs/2312.00752) has been included in our library. See [this file](https://github.com/thuml/Time-Series-Library/blob/main/models/Mamba.py), where you need to install `mamba_ssm` with pip at first. :triangular_flag_on_post:**News** (2024.03) Given the inconsistent look-back length of various papers, we split the long-term forecasting in the leaderboard into two categories: Look-Back-96 and Look-Back-Searching. We recommend researchers read [TimeMixer](https://openreview.net/pdf?id=7oLshfEIC2), which includes both look-back length settings in experiments for scientific rigor. :triangular_flag_on_post:**News** (2023.10) We add an implementation to [iTransformer](https://arxiv.org/abs/2310.06625), which is the state-of-the-art model for long-term forecasting. The official code and complete scripts of iTransformer can be found [here](https://github.com/thuml/iTransformer). :triangular_flag_on_post:**News** (2023.09) We added a detailed [tutorial](https://github.com/thuml/Time-Series-Library/blob/main/tutorial/TimesNet_tutorial.ipynb) for [TimesNet](https://openreview.net/pdf?id=ju_Uqw384Oq) and this library, which is quite friendly to beginners of deep time series analysis. :triangular_flag_on_post:**News** (2023.02) We release the TSlib as a comprehensive benchmark and code base for time series models, which is extended from our previous GitHub repository [Autoformer](https://github.com/thuml/Autoformer). ## Leaderboard for Time Series Analysis Till March 2024, the top three models for five different tasks are: | Model
Ranking | Long-term
Forecasting
Look-Back-96 | Long-term
Forecasting
Look-Back-Searching | Short-term
Forecasting | Imputation | Classification | Anomaly
Detection | | ---------------- | ----------------------------------------------------- | ----------------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | -------------------------------------------------- | | 🥇 1st | [TimeXer](https://arxiv.org/abs/2402.19072) | [TimeMixer](https://openreview.net/pdf?id=7oLshfEIC2) | [TimesNet](https://arxiv.org/abs/2210.02186) | [TimesNet](https://arxiv.org/abs/2210.02186) | [TimesNet](https://arxiv.org/abs/2210.02186) | [TimesNet](https://arxiv.org/abs/2210.02186) | | 🥈 2nd | [iTransformer](https://arxiv.org/abs/2310.06625) | [PatchTST](https://github.com/yuqinie98/PatchTST) | [Non-stationary
Transformer](https://github.com/thuml/Nonstationary_Transformers) | [Non-stationary
Transformer](https://github.com/thuml/Nonstationary_Transformers) | [Non-stationary
Transformer](https://github.com/thuml/Nonstationary_Transformers) | [FEDformer](https://github.com/MAZiqing/FEDformer) | | 🥉 3rd | [TimeMixer](https://openreview.net/pdf?id=7oLshfEIC2) | [DLinear](https://arxiv.org/pdf/2205.13504.pdf) | [FEDformer](https://github.com/MAZiqing/FEDformer) | [Autoformer](https://github.com/thuml/Autoformer) | [Informer](https://github.com/zhouhaoyi/Informer2020) | [Autoformer](https://github.com/thuml/Autoformer) | **Note: We will keep updating this leaderboard.** If you have proposed advanced and awesome models, you can send us your paper/code link or raise a pull request. We will add them to this repo and update the leaderboard as soon as possible. **Compared models of this leaderboard.** ☑ means that their codes have already been included in this repo. - [x] **TimeXer** - TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables [[NeurIPS 2024]](https://arxiv.org/abs/2402.19072) [[Code]](https://github.com/thuml/Time-Series-Library/blob/main/models/TimeXer.py) - [x] **TimeMixer** - TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting [[ICLR 2024]](https://openreview.net/pdf?id=7oLshfEIC2) [[Code]](https://github.com/thuml/Time-Series-Library/blob/main/models/TimeMixer.py). - [x] **TSMixer** - TSMixer: An All-MLP Architecture for Time Series Forecasting [[arXiv 2023]](https://arxiv.org/pdf/2303.06053.pdf) [[Code]](https://github.com/thuml/Time-Series-Library/blob/main/models/TSMixer.py) - [x] **iTransformer** - iTransformer: Inverted Transformers Are Effective for Time Series Forecasting [[ICLR 2024]](https://arxiv.org/abs/2310.06625) [[Code]](https://github.com/thuml/Time-Series-Library/blob/main/models/iTransformer.py). - [x] **PatchTST** - A Time Series is Worth 64 Words: Long-term Forecasting with Transformers [[ICLR 2023]](https://openreview.net/pdf?id=Jbdc0vTOcol) [[Code]](https://github.com/thuml/Time-Series-Library/blob/main/models/PatchTST.py). - [x] **TimesNet** - TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis [[ICLR 2023]](https://openreview.net/pdf?id=ju_Uqw384Oq) [[Code]](https://github.com/thuml/Time-Series-Library/blob/main/models/TimesNet.py). - [x] **DLinear** - Are Transformers Effective for Time Series Forecasting? [[AAAI 2023]](https://arxiv.org/pdf/2205.13504.pdf) [[Code]](https://github.com/thuml/Time-Series-Library/blob/main/models/DLinear.py). - [x] **LightTS** - Less Is More: Fast Multivariate Time Series Forecasting with Light Sampling-oriented MLP Structures [[arXiv 2022]](https://arxiv.org/abs/2207.01186) [[Code]](https://github.com/thuml/Time-Series-Library/blob/main/models/LightTS.py). - [x] **ETSformer** - ETSformer: Exponential Smoothing Transformers for Time-series Forecasting [[arXiv 2022]](https://arxiv.org/abs/2202.01381) [[Code]](https://github.com/thuml/Time-Series-Library/blob/main/models/ETSformer.py). - [x] **Non-stationary Transformer** - Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting [[NeurIPS 2022]](https://openreview.net/pdf?id=ucNDIDRNjjv) [[Code]](https://github.com/thuml/Time-Series-Library/blob/main/models/Nonstationary_Transformer.py). - [x] **FEDformer** - FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting [[ICML 2022]](https://proceedings.mlr.press/v162/zhou22g.html) [[Code]](https://github.com/thuml/Time-Series-Library/blob/main/models/FEDformer.py). - [x] **Pyraformer** - Pyraformer: Low-complexity Pyramidal Attention for Long-range Time Series Modeling and Forecasting [[ICLR 2022]](https://openreview.net/pdf?id=0EXmFzUn5I) [[Code]](https://github.com/thuml/Time-Series-Library/blob/main/models/Pyraformer.py). - [x] **Autoformer** - Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting [[NeurIPS 2021]](https://openreview.net/pdf?id=I55UqU-M11y) [[Code]](https://github.com/thuml/Time-Series-Library/blob/main/models/Autoformer.py). - [x] **Informer** - Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting [[AAAI 2021]](https://ojs.aaai.org/index.php/AAAI/article/view/17325/17132) [[Code]](https://github.com/thuml/Time-Series-Library/blob/main/models/Informer.py). - [x] **Reformer** - Reformer: The Efficient Transformer [[ICLR 2020]](https://openreview.net/forum?id=rkgNKkHtvB) [[Code]](https://github.com/thuml/Time-Series-Library/blob/main/models/Reformer.py). - [x] **Transformer** - Attention is All You Need [[NeurIPS 2017]](https://proceedings.neurips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf) [[Code]](https://github.com/thuml/Time-Series-Library/blob/main/models/Transformer.py). See our latest paper [[TimesNet]](https://arxiv.org/abs/2210.02186) for the comprehensive benchmark. We will release a real-time updated online version soon. **Newly added baselines.** We will add them to the leaderboard after a comprehensive evaluation. - [x] **TimeFilter** - TimeFilter: Patch-Specific Spatial-Temporal Graph Filtration for Time Series Forecasting [[ICML 2025]](https://arxiv.org/abs/2501.13041) [[Code]](https://github.com/thuml/Time-Series-Library/blob/main/models/TimeFilter.py) - [x] **KAN-AD** - KAN-AD: Time Series Anomaly Detection with Kolmogorov-Arnold Networks [[ICML 2025]](https://arxiv.org/abs/2411.00278) [[Code]](https://github.com/thuml/Time-Series-Library/blob/main/models/KANAD.py) - [x] **MultiPatchFormer** - A multiscale model for multivariate time series forecasting [[Scientific Reports 2025]](https://www.nature.com/articles/s41598-024-82417-4) [[Code]](https://github.com/thuml/Time-Series-Library/blob/main/models/MultiPatchFormer.py) - [x] **WPMixer** - WPMixer: Efficient Multi-Resolution Mixing for Long-Term Time Series Forecasting [[AAAI 2025]](https://arxiv.org/abs/2412.17176) [[Code]](https://github.com/thuml/Time-Series-Library/blob/main/models/WPMixer.py) - [x] **MSGNet** - MSGNet: Learning Multi-Scale Inter-Series Correlations for Multivariate Time Series Forecasting [[AAAI 2024]](https://dl.acm.org/doi/10.1609/aaai.v38i10.28991) [[Code]](https://github.com/thuml/Time-Series-Library/blob/main/models/MSGNet.py) - [x] **PAttn** - Are Language Models Actually Useful for Time Series Forecasting? [[NeurIPS 2024]](https://arxiv.org/pdf/2406.16964) [[Code]](https://github.com/thuml/Time-Series-Library/blob/main/models/PAttn.py) - [x] **Mamba** - Mamba: Linear-Time Sequence Modeling with Selective State Spaces [[arXiv 2023]](https://arxiv.org/abs/2312.00752) [[Code]](https://github.com/thuml/Time-Series-Library/blob/main/models/Mamba.py) - [x] **SegRNN** - SegRNN: Segment Recurrent Neural Network for Long-Term Time Series Forecasting [[arXiv 2023]](https://arxiv.org/abs/2308.11200.pdf) [[Code]](https://github.com/thuml/Time-Series-Library/blob/main/models/SegRNN.py). - [x] **Koopa** - Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors [[NeurIPS 2023]](https://arxiv.org/pdf/2305.18803.pdf) [[Code]](https://github.com/thuml/Time-Series-Library/blob/main/models/Koopa.py). - [x] **FreTS** - Frequency-domain MLPs are More Effective Learners in Time Series Forecasting [[NeurIPS 2023]](https://arxiv.org/pdf/2311.06184.pdf) [[Code]](https://github.com/thuml/Time-Series-Library/blob/main/models/FreTS.py). - [x] **MICN** - MICN: Multi-scale Local and Global Context Modeling for Long-term Series Forecasting [[ICLR 2023]](https://openreview.net/pdf?id=zt53IDUR1U)[[Code]](https://github.com/thuml/Time-Series-Library/blob/main/models/MICN.py). - [x] **Crossformer** - Crossformer: Transformer Utilizing Cross-Dimension Dependency for Multivariate Time Series Forecasting [[ICLR 2023]](https://openreview.net/pdf?id=vSVLM2j9eie)[[Code]](https://github.com/thuml/Time-Series-Library/blob/main/models/Crossformer.py). - [x] **TiDE** - Long-term Forecasting with TiDE: Time-series Dense Encoder [[arXiv 2023]](https://arxiv.org/pdf/2304.08424.pdf) [[Code]](https://github.com/thuml/Time-Series-Library/blob/main/models/TiDE.py). - [x] **SCINet** - SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction [[NeurIPS 2022]](https://openreview.net/pdf?id=AyajSjTAzmg)[[Code]](https://github.com/thuml/Time-Series-Library/blob/main/models/SCINet.py). - [x] **FiLM** - FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting [[NeurIPS 2022]](https://openreview.net/forum?id=zTQdHSQUQWc)[[Code]](https://github.com/thuml/Time-Series-Library/blob/main/models/FiLM.py). - [x] **TFT** - Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting [[arXiv 2019]](https://arxiv.org/abs/1912.09363)[[Code]](https://github.com/thuml/Time-Series-Library/blob/main/models/TemporalFusionTransformer.py). **Newly added Large Time Series Models.** This library also supports the zero-shot evaluation of the following LTSMs. - [x] **Chronos2** - Chronos-2: From Univariate to Universal Forecasting [[arXiv 2025]](https://arxiv.org/abs/2510.15821) [[Code]](https://github.com/thuml/Time-Series-Library/blob/main/models/Chronos2.py) - [x] **TiRex** - TiRex: Zero-Shot Forecasting Across Long and Short Horizons with Enhanced In-Context Learning [[NeurIPS 2025]](https://arxiv.org/pdf/2505.23719) [[Code]](https://github.com/thuml/Time-Series-Library/blob/main/models/TiRex.py) - [x] **Sundial** - Sundial: A Family of Highly Capable Time Series Foundation Models [[ICML 2025]](https://arxiv.org/pdf/2502.00816) [[Code]](https://github.com/thuml/Time-Series-Library/blob/main/models/Sundial.py) - [x] **Time-MoE** - Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts [[ICLR 2025]](https://arxiv.org/pdf/2409.16040) [[Code]](https://github.com/thuml/Time-Series-Library/blob/main/models/TimeMoE.py) - [x] **Toto** - Toto: Time Series Optimized Transformer for Observability [arXiv 2024](https://arxiv.org/pdf/2407.07874) - [x] **Chronos** - Chronos: Learning the Language of Time Series [[TMLR 2024]](https://arxiv.org/pdf/2403.07815) [[Code]](https://github.com/thuml/Time-Series-Library/blob/main/models/Chronos.py) - [x] **Moirai** - Unified Training of Universal Time Series Forecasting Transformers [[ICML 2024]](https://arxiv.org/pdf/2402.02592) - [x] **TimesFM** - A decoder-only foundation model for time-series forecasting [[ICML 2024]](https://arxiv.org/abs/2310.10688) [[Code]](https://github.com/thuml/Time-Series-Library/blob/main/models/TimesFM.py) ## Getting Started ### Prepare Data You can obtain the well-preprocessed datasets from [[Google Drive]](https://drive.google.com/drive/folders/13Cg1KYOlzM5C7K8gK8NfC-F3EYxkM3D2?usp=sharing), [[Baidu Drive]](https://pan.baidu.com/s/1r3KhGd0Q9PJIUZdfEYoymg?pwd=i9iy) or [[Hugging Face]](https://huggingface.co/datasets/thuml/Time-Series-Library). Then place the downloaded data in the folder `./dataset`. ### Installation 1. Clone this repository. ```bash git clone https://github.com/thuml/Time-Series-Library.git cd Time-Series-Library ``` 2. Create a new Conda environment. ```bash conda create -n tslib python=3.11 conda activate tslib ``` 3. Install Core Dependencies ```bash pip install -r requirements.txt ``` 4. Install Dependencies for Mamba Model (Required for Time-Series-Library/models/Mamba.py) > ⚠️ **CUDA Compatibility Notice** > The prebuilt Mamba wheel is **CUDA-version specific**. > Please make sure to install the wheel that matches your local CUDA version > (e.g., `cu11` or `cu12`). Installing a mismatched version may result in > runtime errors or import failures. Example for **CUDA 12**: ```bash pip install https://github.com/state-spaces/mamba/releases/download/v2.2.6.post3/mamba_ssm-2.2.6.post3+cu12torch2.5cxx11abiFALSE-cp311-cp311-linux_x86_64.whl ``` 5. Install Dependencies for Moirai Model (Required for Time-Series-Library/models/Moirai.py) ```bash pip install uni2ts --no-deps ``` ### Docker Deployment ```bash # Build and start the Docker container in detached mode docker compose -f 'Time-Series-Library/docker-compose.yml' up -d --build # Download / place the dataset into a newly created folder ./dataset at the repository root mkdir -p dataset # create the dataset directory # Copy the local dataset into the container at /workspace/dataset docker cp ./dataset tslib:/workspace/dataset # Enter the running container to continue training / evaluation docker exec -it tslib bash # Switch to the workspace directory inside the container cd /workspace # Run zero-shot forecasting with the pre-trained Moirai model python -u run.py \ --task_name zero_shot_forecast \ # task type: zero-shot forecasting --is_training 0 \ # 0 = inference only (no training) --root_path ./dataset/ETT-small/ \ # root directory of the dataset --data_path ETTh1.csv \ # dataset file name --model_id ETTh1_512_96 \ # experiment/model identifier --model Moirai \ # model name (TimesFM / Moirai) --data ETTh1 \ # dataset name --features M \ # multivariate forecasting --seq_len 512 \ # input sequence length --pred_len 96 \ # prediction horizon --enc_in 7 \ # number of input variables --des 'Exp' \ # experiment description --itr 1 # number of runs ``` ### Quick Test Quick test for all 5 tasks (1 epoch each): ```bash # Run quick tests for all 5 tasks export CUDA_VISIBLE_DEVICES=0 # 1. Long-term forecasting python -u run.py --task_name long_term_forecast --is_training 1 --root_path ./dataset/ETT-small/ --data_path ETTh1.csv --model_id test_long --model DLinear --data ETTh1 --features M --seq_len 96 --pred_len 96 --enc_in 7 --dec_in 7 --c_out 7 --train_epochs 1 --num_workers 2 # 2. Short-term forecasting (using ETT dataset with shorter prediction length) python -u run.py --task_name long_term_forecast --is_training 1 --root_path ./dataset/ETT-small/ --data_path ETTh1.csv --model_id test_short --model TimesNet --data ETTh1 --features M --seq_len 24 --label_len 12 --pred_len 24 --e_layers 2 --d_layers 1 --d_model 16 --d_ff 32 --enc_in 7 --dec_in 7 --c_out 7 --top_k 5 --train_epochs 1 --num_workers 2 # 3. Imputation python -u run.py --task_name imputation --is_training 1 --root_path ./dataset/ETT-small/ --data_path ETTh1.csv --model_id test_imp --model TimesNet --data ETTh1 --features M --seq_len 96 --e_layers 2 --d_layers 1 --d_model 16 --d_ff 32 --enc_in 7 --dec_in 7 --c_out 7 --top_k 3 --train_epochs 1 --num_workers 2 --label_len 0 --pred_len 0 --mask_rate 0.125 --learning_rate 0.001 # 4. Anomaly detection python -u run.py --task_name anomaly_detection --is_training 1 --root_path ./dataset/PSM --model_id test_ad --model TimesNet --data PSM --features M --seq_len 100 --pred_len 0 --d_model 64 --d_ff 64 --e_layers 2 --enc_in 25 --c_out 25 --anomaly_ratio 1.0 --top_k 3 --train_epochs 1 --batch_size 128 --num_workers 2 # 5. Classification python -u run.py --task_name classification --is_training 1 --root_path ./dataset/Heartbeat/ --model_id Heartbeat --model TimesNet --data UEA --e_layers 2 --d_layers 1 --factor 3 --d_model 64 --d_ff 128 --top_k 3 --train_epochs 1 --batch_size 16 --learning_rate 0.001 --num_workers 0 ``` ### Train and Evaluate We provide the experiment scripts for all benchmarks under the folder `./scripts/`. You can reproduce the experiment results as the following examples: ```bash # long-term forecast bash ./scripts/long_term_forecast/ETT_script/TimesNet_ETTh1.sh # short-term forecast bash ./scripts/short_term_forecast/TimesNet_M4.sh # imputation bash ./scripts/imputation/ETT_script/TimesNet_ETTh1.sh # anomaly detection bash ./scripts/anomaly_detection/PSM/TimesNet.sh # classification bash ./scripts/classification/TimesNet.sh ``` ### Develop Your Own Model - Add the model file to the folder `./models`. You can follow the `./models/Transformer.py`. - Include the newly added model in the `Exp_Basic.model_dict` of `./exp/exp_basic.py`. - Create the corresponding scripts under the folder `./scripts`. ### Note: (1) About classification: Since we include all five tasks in a unified code base, the accuracy of each subtask may fluctuate but the average performance can be reproduced (even a bit better). We have provided the reproduced checkpoints [here](https://github.com/thuml/Time-Series-Library/issues/494). (2) About anomaly detection: Some discussion about the adjustment strategy in anomaly detection can be found [here](https://github.com/thuml/Anomaly-Transformer/issues/14). The key point is that the adjustment strategy corresponds to an event-level metric. ### Inspect the project structure: ``` Time-Series-Library/ ├── README.md # Official README with tasks, leaderboard, usage ├── requirements.txt # pip dependency list for quick environment setup ├── LICENSE / CONTRIBUTING.md # Upstream license and contribution guide ├── run.py # Unified entry that parses args and dispatches tasks ├── exp/ # Task pipelines wrapping train/val/test │ ├── exp_basic.py # Experiment base class, registers models, builds flows │ ├── exp_long_term_forecasting.py # Long-term forecasting logic │ ├── exp_short_term_forecasting.py # Short-term forecasting logic │ ├── exp_imputation.py # Missing-value imputation │ ├── exp_anomaly_detection.py # Anomaly detection │ ├── exp_classification.py # Classification │ └── exp_zero_shot_forecasting.py # LTSM zero-shot evaluation ├── data_provider/ # Dataset loaders and splits │ ├── data_factory.py # Chooses the proper DataLoader per task │ ├── data_loader.py # Generic TS reader with sliding-window logic │ ├── uea.py / m4.py # Parsers for UEA, M4 and other formats │ └── __init__.py # Exposes factory interfaces upward ├── models/ # All model implementations │ ├── TimesNet.py, TimeMixer.py # Main forecasting models │ ├── Chronos2.py, TiRex.py # LTSM zero-shot models │ └── __init__.py # Enables name-based instantiation inside exp ├── layers/ # Reusable attention / conv / embedding blocks │ ├── Transformer_EncDec.py # Transformer stacks │ ├── AutoCorrelation.py # Auto-correlation operator │ ├── MultiWaveletCorrelation.py# Frequency-domain unit │ └── Embed.py etc. # Shared primitives ├── utils/ # Utility toolbox │ ├── metrics.py # MSE / MAE / DTW and other metrics │ ├── tools.py # General helpers such as EarlyStopping │ ├── augmentation.py # Augmentations for classification / detection │ ├── print_args.py # Unified argument printer │ └── masking.py / losses.py # Task-specific helpers ├── scripts/ # Bash recipes for reproducible experiments │ ├── long_term_forecast/ # Long-term forecasting per dataset/model │ ├── short_term_forecast/ # M4 and other short-term scripts │ ├── imputation/ # Imputation scripts │ ├── anomaly_detection/ # SMD / SMAP / SWAT detection scripts │ ├── classification/ # UEA classification scripts │ └── exogenous_forecast/ # TimeXer exogenous forecasting flow ├── tutorial/ # TimesNet tutorial notebook and figures └── pic/ # README figures (dataset overview, etc.) ``` ### Understand the project architecture: - **E2E flow**: configure experiments via `scripts/*.sh` → run `python run.py ...` → `run.py` parses arguments and selects the proper `Exp_*` via `task_name` → the experiment builds datasets through `data_provider`, instantiates networks from `models`, and drives train/val/test with utilities in `utils` → metrics and checkpoints are written to `./checkpoints`. - **Experiment layer (`exp/`)**: `Exp_Basic` registers models and devices; subclasses implement `_get_data`, `train`, and `test` to encapsulate task-specific differences so the same model can be reused. - **Model & layer layer (`models/` + `layers/`)**: model files define architectures, while reusable attention/conv/frequency components live in `layers/` to minimize duplication. - **Data layer (`data_provider/`)**: `data_factory` returns the correct `Dataset/DataLoader`; `data_loader` handles windowing, masking, and sampling, with arguments controlling window length, missing ratio, anomaly ratio, etc. - **Script layer (`scripts/`)**: bash scripts capture paper configurations (dataset, window, model, GPU) for reproducibility and serve as templates for custom runs. - **Utility layer (`utils/`)**: `metrics` centralizes evaluation, `tools` bundles essentials like `EarlyStopping` and `adjust_learning_rate`, while `augmentation`/`masking` cover task-specific preprocessing. - **Learning path**: recommended reading order is `scripts -> run.py -> exp/exp_basic.py -> corresponding Exp subclass -> data_provider -> models`, using `tutorial/TimesNet_tutorial.ipynb` as a guided walkthrough before diving deeper. ## Citation If you find this repo useful, please cite our paper. ``` @inproceedings{wu2023timesnet, title={TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis}, author={Haixu Wu and Tengge Hu and Yong Liu and Hang Zhou and Jianmin Wang and Mingsheng Long}, booktitle={International Conference on Learning Representations}, year={2023}, } @article{wang2024tssurvey, title={Deep Time Series Models: A Comprehensive Survey and Benchmark}, author={Yuxuan Wang and Haixu Wu and Jiaxiang Dong and Yong Liu and Mingsheng Long and Jianmin Wang}, booktitle={arXiv preprint arXiv:2407.13278}, year={2024}, } ``` ## Contact If you have any questions or suggestions, feel free to contact our maintenance team: Current: - Haixu Wu (Ph.D., wuhaixu98@gmail.com) - Yuxuan Wang (Ph.D. student, wangyuxu22@mails.tsinghua.edu.cn) - Yong Liu (Ph.D. student, liuyong21@mails.tsinghua.edu.cn) - Ailuntz (Student from Open-source Community, ailuntz@icloud.com) Previous: - Huikun Weng (Undergraduate, wenghk22@mails.tsinghua.edu.cn) - Tengge Hu (Master student, htg21@mails.tsinghua.edu.cn) - Haoran Zhang (Master student, z-hr20@mails.tsinghua.edu.cn) - Jiawei Guo (Undergraduate, guo-jw21@mails.tsinghua.edu.cn) Or describe it in Issues. ## Acknowledgement This library is constructed based on the following repos: - Forecasting: https://github.com/thuml/Autoformer. - Anomaly Detection: https://github.com/thuml/Anomaly-Transformer. - Classification: https://github.com/thuml/Flowformer. All the experiment datasets are public, and we obtain them from the following links: - Long-term Forecasting and Imputation: https://github.com/thuml/Autoformer. - Short-term Forecasting: https://github.com/ServiceNow/N-BEATS. - Anomaly Detection: https://github.com/thuml/Anomaly-Transformer. - Classification: https://www.timeseriesclassification.com/. ## All Thanks To Our Contributors