# BasicTS
**Repository Path**: cpy1998/BasicTS
## Basic Information
- **Project Name**: BasicTS
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-04-09
- **Last Updated**: 2024-04-09
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
A Standard and Fair Time Series Forecasting Benchmark and Toolkit.
---
[](https://github.com/cnstark/easytorch)
[](https://github.com/zezhishao/BasicTS/blob/master/LICENSE)
[](https://pytorch.org/)
[](https://github.com/zezhishao/BasicTS/blob/master/.github/workflows/pylint.yml)
$\text{BasicTS}^{+}$ (**Basic** **T**ime **S**eries **P**lus) is an enhanced benchmark and toolbox designed for time series forecasting. $\text{BasicTS}^{+}$ evolved from its predecessor, [BasicTS](https://github.com/zezhishao/BasicTS/blob/v1/README.md), and now has robust support for spatial-temporal forecasting and long time-series forecasting as well as more general tasks, such as M4 competition. For brevity and consistency, we will interchangeably refer to this project as $\text{BasicTS}^{+}$ and $\text{BasicTS}$.
On the one hand, BasicTS utilizes a ***unified and standard pipeline*** to give a ***fair and exhaustive*** reproduction and comparison of popular deep learning-based models.
On the other hand, BasicTS provides users with ***easy-to-use and extensible interfaces*** to facilitate the quick design and evaluation of new models. At a minimum, users only need to define the model architecture.
We are collecting **TODOs** and **HOWTOs**, if you need more features (*e.g.* more datasets or baselines) or have any questions, please feel free to create an issue or leave a comment [here](https://github.com/zezhishao/BasicTS/issues/95).
If you find this repository useful for your work, please consider citing it as [such](./citation.bib).
## β¨ Highlighted Features
### Fair Performance Review
Users can compare the performance of different models on arbitrary datasets fairly and exhaustively based on a unified and comprehensive pipeline.
### Developing with BasicTS
Minimum Code
Users only need to implement key codes such as model architecture and data pre/post-processing to build their own deep learning projects.
Everything Based on Config
Users can control all the details of the pipeline through a config file, such as the hyperparameter of dataloaders, optimization, and other tricks (*e.g.*, curriculum learning).
Support All Devices
BasicTS supports CPU, GPU and GPU distributed training (both single node multiple GPUs and multiple nodes) thanks to using EasyTorch as the backend. Users can use it by setting parameters without modifying any code.
Save Training Log
Support `logging` log system and `Tensorboard`, and encapsulate it as a unified interface, users can save customized training logs by calling simple interfaces.
## π¦ Built-in Datasets and Baselines
### Datasets
BasicTS support a variety of datasets, including spatial-temporal forecasting, long time-series forecasting, and large-scale datasets, e.g.,
- METR-LA, PEMS-BAY, PEMS03, PEMS04, PEMS07, PEMS08
- ETTh1, ETTh2, ETTm1, ETTm2, Electricity, Exchange Rate, Weather, Traffic, Illness, Beijing Air Quality
- SD, GLA, GBA, CA
- ...
### Baselines
BasicTS implements a wealth of models, including classic models, spatial-temporal forecasting models, and long time-series forecasting model, e.g.,
- HI, DeepAR, LightGBM, ...
- DCRNN, Graph WaveNet, MTGNN, STID, D2STGNN, STEP, DGCRN, DGCRN, STNorm, AGCRN, GTS, StemGNN, MegaCRN, STGCN, STWave, STAEformer, GMSDR, ...
- Informer, Autoformer, FEDformer, Pyraformer, DLinear, NLinear, Triformer, Crossformer, ...
## πΏ Dependencies
Preliminaries
### OS
We recommend using BasicTS on Linux systems (*e.g.* Ubuntu and CentOS).
Other systems (*e.g.*, Windows and macOS) have not been tested.
### Python
Python >= 3.6 (recommended >= 3.9).
[Miniconda](https://docs.conda.io/en/latest/miniconda.html) or [Anaconda](https://www.anaconda.com/) are recommended to create a virtual python environment.
### Other Dependencies
BasicTS is built based on PyTorch and [EasyTorch](https://github.com/cnstark/easytorch).
You can install PyTorch following the instruction in [PyTorch](https://pytorch.org/get-started/locally/). For example:
```bash
pip install torch==1.10.0+cu111 torchvision==0.11.0+cu111 torchaudio==0.10.0 -f https://download.pytorch.org/whl/torch_stable.html
```
**After ensuring** that PyTorch is installed correctly, you can install other dependencies via:
```bash
pip install -r requirements.txt
```
### Warning
BasicTS is built on PyTorch 1.9.1 or 1.10.0, while other versions have not been tested.
## π― Getting Started of Developing with BasicTS
### Preparing Data
- **Clone BasicTS**
```bash
cd /path/to/your/project
git clone https://github.com/zezhishao/BasicTS.git
```
- **Download Raw Data**
You can download all the raw datasets at [Google Drive](https://drive.google.com/drive/folders/14EJVODCU48fGK0FkyeVom_9lETh80Yjp) or [Baidu Yun](https://pan.baidu.com/s/10gOPtlC9M4BEjx89VD1Vbw)(password: 6v0a), and unzip them to `datasets/raw_data/`.
- **Pre-process Data**
```bash
cd /path/to/your/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/${DATASET_NAME}`.
### 3 Steps to Evaluate Your Model
- **Define Your Model Architecture**
The `forward` function needs to follow the conventions of BasicTS. You can find an example of the Multi-Layer Perceptron (`MLP`) model in [baselines/MLP/mlp_arch.py](baselines/MLP/mlp_arch.py)
- **Define Your Runner for Your Model** (Optional)
BasicTS provides a unified and standard pipeline in `basicts.runner.BaseTimeSeriesForecastingRunner`.
Nevertheless, you still need to define the specific forward process (the `forward` function in the **runner**).
Fortunately, BasicTS also provides such an implementation in `basicts.runner.SimpleTimeSeriesForecastingRunner`, which can cover most of the situations.
The runner for the `MLP` model can also use this built-in runner.
You can also find more runners in `basicts.runners.runner_zoo` to learn more about the runner design.
- **Configure your Configuration File**
You can configure all the details of the pipeline and hyperparameters in a configuration file, *i.e.*, **everything is based on config**.
The configuration file is a `.py` file, in which you can import your model and runner and set all the options. BasicTS uses `EasyDict` to serve as a parameter container, which is extensible and flexible to use.
An example of the configuration file for the `MLP` model on the `METR-LA` dataset can be found in [baselines/MLP/MLP_METR-LA.py](baselines/MLP/MLP_METR-LA.py)
### Run It!
- **Reproducing Built-in Models**
BasicTS provides a wealth of built-in models. You can reproduce these models by running the following command:
```bash
python experiments/train.py -c baselines/${MODEL_NAME}/${DATASET_NAME}.py --gpus '0'
```
Replace `${DATASET_NAME}` and `${MODEL_NAME}` with any supported models and datasets. For example, you can run Graph WaveNet on METR-LA dataset by:
```bash
python experiments/train.py -c baselines/GWNet/METR-LA.py --gpus '0'
```
- **Customized Your Own Model**
[Example: Multi-Layer Perceptron (MLP)](baselines/MLP)
## Contributors β¨
Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/docs/en/emoji-key)):
This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome!
## π Main Results
See the paper *[Exploring Progress in Multivariate Time Series Forecasting:
Comprehensive Benchmarking and Heterogeneity Analysis](https://arxiv.org/pdf/2310.06119.pdf).*
## π Acknowledgement
BasicTS is developed based on [EasyTorch](https://github.com/cnstark/easytorch), an easy-to-use and powerful open-source neural network training framework. 
