base on A Library for Advanced Deep Time Series Models. # Time Series Library (TSLib) TSLib is an open-source library for deep learning researchers, especially for deep time series analysis. 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** (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<br>Ranking | Long-term<br>Forecasting<br>Look-Back-96 | Long-term<br/>Forecasting<br/>Look-Back-Searching | Short-term<br>Forecasting | Imputation | Classification | Anomaly<br>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<br/>Transformer](https://github.com/thuml/Nonstationary_Transformers) | [Non-stationary<br/>Transformer](https://github.com/thuml/Nonstationary_Transformers) | [Non-stationary<br/>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] **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). ## Usage 1. Install Python 3.8. For convenience, execute the following command. ``` pip install -r requirements.txt ``` 2. Prepare Data. You can obtain the well pre-processed datasets from [[Google Drive]](https://drive.google.com/drive/folders/13Cg1KYOlzM5C7K8gK8NfC-F3EYxkM3D2?usp=sharing) or [[Baidu Drive]](https://pan.baidu.com/s/1r3KhGd0Q9PJIUZdfEYoymg?pwd=i9iy), Then place the downloaded data in the folder`./dataset`. Here is a summary of supported datasets. <p align="center"> <img src=".\pic\dataset.png" height = "200" alt="" align=center /> </p> 3. Train and evaluate model. We provide the experiment scripts for all benchmarks under the folder `./scripts/`. You can reproduce the experiment results as the following examples: ``` # 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 ``` 4. 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. ## 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. student, [email protected]) - Yong Liu (Ph.D. student, [email protected]) - Yuxuan Wang (Ph.D. student, [email protected]) - Huikun Weng (Undergraduate, [email protected]) Previous: - Tengge Hu (Master student, [email protected]) - Haoran Zhang (Master student, [email protected]) - Jiawei Guo (Undergraduate, [email protected]) Or describe it in Issues. ## Acknowledgement This project is supported by the National Key R&D Program of China (2021YFB1715200). 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 <a href="https://github.com/thuml/Time-Series-Library/graphs/contributors"> <img src="https://contrib.rocks/image?repo=thuml/Time-Series-Library" /> </a> ", Assign "at most 3 tags" to the expected json: {"id":"3861","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"