zhoujiajuly/copy-basicTS

A copy and test for https://github.com/zezhishao/BasicTS

BasicTS

注意这是一个copy 用于自己的科研研究 原作者的地址为：https://github.com/zezhishao/BasicTS 这是一个很好的仓库，请大家多多支持。博主也是一个很棒的人！

0. What is BasicTS

BasicTS (Basic Time Series) is an open-source PyTorch-based time series benchmark and toolbox motivated by BasicSR [1]. At present, it only focuses on time series forecasting, and may add time series classification, anomaly detection, etc., in the future. BasicTS provides users with a unified, standard pipeline (fair, but probably not the fastest), which provides reproduction and fair comparision of popular deep learning-based time series models to inspire new innovations. BasicTS is developed based on EasyTorch [2], an easy-to-use and powerful open source neural network training framework.

BasicTS (Basic Time Series)是一个受BasicSR [1]启发的基于PyTorch的开源时间序列Benchmark和工具箱。 目前仅专注于时间序列预测，后续可能会添加时间序列分类、异常检测等内容。 BasicTS为用户提供 统一的、标准的 pipeline（他是公平的，但可能不是最快的），来提供流行的基于深度学习的时间序列模型的 复现和公平对比 ，以启发新的创新。 BasicTS基于一个易用、强大的开源神经网络训练框架EasyTorch [2]开发。

If this repository helps your research or work, I hope you could give me a ⭐, and I will keep updating it. If you need more features about BasicTS (e.g. more datasets or methods) or have any questions/suggestions, please feel free to let me know~

1. Supported Models and Datasets

1.1 Short-term Time Series Forecasting

Model\Dataset	METR-LA	PEMS-BAY	PEMS04	PEMS08	PEMS03	PEMS07	Other Datasets
AR	✅	✅	✅	✅	✅	✅
VAR	✅	✅	✅	✅	✅	✅
HI	✅	✅	✅	✅	✅	✅
Graph WaveNet	✅	✅	✅	✅	✅	✅
DCRNN	✅	✅	✅	✅	✅	✅
STGCN	✅	✅	✅	✅	✅	✅
StemGNN	✅	✅	✅	✅	✅	✅
MTGNN	✅	✅	✅	✅	✅	✅
GTS	✅	✅	✅	✅	✅	✅
DGCRN	✅	✅	✅	✅	✅	✅
GMAN	✅	✅	✅	✅	✅	✅
AGCRN	✅	✅	✅	✅	✅	✅
STNorm	✅	✅	✅	✅	✅	✅
D2STGNN	✅	✅	✅	✅	✅	✅
Other Models

Although we have tried our best to tune the hyperparameters in basicts/options for every model and every dataset, there is no guarantee that they are optimal. Thus, any PRs for better hyper-parameters are welcomed to make BasicTS fairer.

1.2 Long-term Time Series Forecasting

🕐

2. Installing Dependencies

2.1 Main Dependencies

• python 3.9
• pytorch 1.9.1
• easy-torch latest

⚠️⚠️⚠️ Please use pip install easy-torch, instead of pip install easytorch, to install EasyTorch. They are different two packages. ⚠️⚠️⚠️

2.2 Installing from Pip

pip install -r requirements.txt

3. Codebase Designs and Conventions

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4. Usage

git clone https://github.com/zezhishao/BasicTS.git

4.1 Data Preparation and Preprocessing

4.1.1 Data Preparation

You can download the raw datasets at Google Drive or Baidu Yun(password: 0lrk), and unzip them to datasets/raw_data/.

4.1.2 Data Preprocessing

cd /path/to/project
python scripts/data_preparation/$DATASET_NAME/generate_training_data.py

Replace $DATASET_NAME with one of METR-LA, PEMS-BAY, PEMS03, PEMS04, PEMS07, PEMS08, or any other supported dataset.

The processed data will be placed in datasets/$DATASET_NAME.

Details of preprocessing can be found in docs/DataPreparation_CN.md~(Chinese).

4.2 Run a Time Series Forecasting Model

We recommend running a time series model with the following command:

cd /path/to/project
easytrain -c basicts/options/$METHOD_NAME/$METHOD_NAME_$DATASET_NAME.py --gpus '0'

Replace the $METHOD_NAME and $DATASET_NAME with any supported method and dataset. For example,

easytrain -c basicts/options/GraphWaveNet/GraphWaveNet_METR-LA.py --gpus '0'

If you need to debug, you could run the basicts/run.py file.

4.3 Train a Custom Model

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5. Detailed Docs

• data preparation: data_preparation_CN.md

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6. Main Results

7. TODO

• : revise the data preprocessing of PEMS07, which only contains weekdays.
• : Add detailed documentation and a demo about data preprocessing.
• : Add more multivariate time series datasets: Traffic, Electricity, Exchange-Rate, ETTh1, ETTh2, ETTm1, Weather, Solar-Energy.
• : Different from the existing traffic datasets (PEMS0X, PEMS-BAY, METR-LA), these datasets have multiple usages in the existing datasets, and the baselines that need to be compared in different contexts are different. Therefore, it is necessary to add statistics for all datasets and describe their typical settings case by case.
• : Add statistical information of these dataset, and descibe their typical settings.
• : Support models like ASTGCN, ASTGNN, which take multi-periodicities data as input.
• : Add detailed docs about 4.2, e.g., the usage of gpu.

References

[1] Xintao Wang, Ke Yu, Kelvin C.K. Chan, Chao Dong and Chen Change Loy. BasicSR: Open Source Image and Video Restoration Toolbox. https://github.com/xinntao/BasicSR, 2018.
[2] Yuhao Wang. EasyTorch. https://github.com/cnstark/easytorch, 2020.