Awesome
WaveForM
This is a PyTorch implementation of the paper: WaveForM: Graph Enhanced Wavelet Learning for Long Sequence Forecasting of Multivariate Time Series, published in AAAI 2023.
Requirements
To run the code, you need to install Python(>=3.9.12) and PyTorch(>=1.11.0) at least. The full requirements are specified in requirements.txt.
The pytorch_wavelets package should be installed following the instructions of pytorch_wavelets.
Data
The Electricity, Solar-energy and Traffic datasets can be downloaded from multivariate-time-series-data.
The Weather datasets can be downloaded from Autoformer.
The Temperature datasets (asos) can be downloaded from spacetimeformer.
You should put the xx.csv file into the directory with the datasets' name in dataset directory.
For example, the proper file structure should be like:
dataset
|-- electricity
| |-- electricity.csv
|--solar
| |-- solar.csv
|--temperature
| |-- temperature.csv
|--traffic
| |-- traffic.csv
|--weather
| |-- weather.csv
Running the code
The running script is run.sh, where you can change any arguments which have been declared in run.py.
Contact
If you have any questions, you can raise an issue or send an e-mail to yfh@bit.edu.cn.
Acknowledgement
Thanks to the following repos for their codes and datasets.
https://github.com/fbcotter/pytorch_wavelets
https://github.com/thuml/Autoformer