# timesNet

**Repository Path**: zhanghel20/times-net

## Basic Information

- **Project Name**: timesNet
- **Description**: Forked from https://github.com/thuml/Time-Series-Library.git
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 1
- **Forks**: 0
- **Created**: 2023-03-12
- **Last Updated**: 2023-03-14

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Time Series Library (TSlib)
TSlib is an open-source library for deep learning researchers, especially deep time series analysis.

We provide a neat code base to evaluate advanced deep time series models or develop your own model, which covers five mainstream tasks: **long- and short-term forecasting, imputation, anomaly detection, and classification.**

## Leaderboard for Time Series Analysis

Till February 2023, the top three models for five different tasks are:

| Model<br>Ranking | Long-term<br>Forecasting                                     | Short-term<br>Forecasting                                    | Imputation                                                   | Anomaly<br>Detection                                         | Classification                                     |
| ---------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | -------------------------------------------------- |
| 🥇 1st            | [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)                 | [TimesNet](https://arxiv.org/abs/2210.02186)       |
| 🥈 2nd            | [DLinear](https://github.com/cure-lab/LTSF-Linear)           | [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            | [Non-stationary<br>Transformer](https://github.com/thuml/Nonstationary_Transformers) | [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 leaderborad.** If you have proposed advanced and awesome models, welcome to send your paper/code link to us or raise a pull request. We will add them to this repo and update the leaderborad as soon as possible.

**Compared models of this leaderboard.** ☑ means that their codes have already been included in this repo.

  - [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 in March.

**Newly added baselines.** We will add them into the leaderboard after a comprehensive evaluation.
  - [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] **MICN** - MICN: Multi-scale Local and Global Context Modeling for Long-term Series Forecasting [[ICLR2023]](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 [[ICLR2023]](https://openreview.net/pdf?id=vSVLM2j9eie)[[Code]](https://github.com/thuml/Time-Series-Library/blob/main/models/Crossformer.py)
 
## Usage

1. Install Python 3.8. For convenience, execute the following command.

```
pip install -r requirements.txt
```

2. Prepare Data. You can obtained the well pre-processed datasets from [[Google Drive]](https://drive.google.com/drive/folders/13Cg1KYOlzM5C7K8gK8NfC-F3EYxkM3D2?usp=sharing), [[Tsinghua Cloud]](https://cloud.tsinghua.edu.cn/f/84fbc752d0e94980a610/) or [[Baidu Drive]](https://pan.baidu.com/s/1r3KhGd0Q9PJIUZdfEYoymg?pwd=i9iy). Then place the downloaded data under 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 of 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`.

## 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},
}
```

## Contact
If you have any questions or suggestions, feel free to contact:

- Haixu Wu (whx20@mails.tsinghua.edu.cn)
- Tengge Hu (htg21@mails.tsinghua.edu.cn)
- Haoran Zhang (z-hr20@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/