FilterNet (NeurIPS 2024)
January 30, 2025 · View on GitHub
The repo is the official implementation for the paper: "FilterNet: Harnessing Frequency Filters for Time Series Forecasting".
Getting Started
1、Environment Requirements
To get started, ensure you have Conda installed on your system and follow these steps to set up the environment:
conda create -n FilterNet python=3.8
conda activate FilterNet
pip install -r requirements.txt
2、Download Data
All the datasets needed for FilterNet can be obtained from the Google Drive provided in Autoformer.
3、Training Example
For datasets with a small number of variables, such as ETTh, ETTm, and Exchange, we recommend using PaiFilter as follows:
bash ./scripts/PaiFilter/ETTm1.sh
bash ./scripts/PaiFilter/ETTm2.sh
bash ./scripts/PaiFilter/ETTh2.sh
For datasets with a large number of variables such as ECL, Traffic, and weather, it is recommended to use TexFilter as follows:
bash ./scripts/PaiFilter/ECL.sh
bash ./scripts/PaiFilter/Traffic.sh
bash ./scripts/PaiFilter/Weather.sh
Updates
👉 News (2024.12): Another one of our recent works, Amplifier: Bringing Attention to Neglected Low-Energy Components in Time Series Forecasting, has been accepted by AAAI 2025.
Our Other Work about Learning in the Frequency Domain for Time Series Analysis
🚩 [IJCAI 2024]: Deep Frequency Derivative Learning for Non-stationary Time Series Forecasting
🚩 [NeurIPS 2023]: Frequency-domain MLPs are more effective learners in time series forecasting
🚩 [NeurIPS 2023]: FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective
🚩 [arXiv]: A Survey on Deep Learning based Time Series Analysis with Frequency Transformation
Acknowledgement
We appreciate the following GitHub repositories for providing valuable code bases and datasets:
https://github.com/wanghq21/MICN
https://github.com/thuml/TimesNet
https://github.com/aikunyi/FreTS
https://github.com/VEWOXIC/FITS
https://github.com/plumprc/RTSF
https://github.com/cure-lab/LTSF-Linear
https://github.com/zhouhaoyi/Informer2020
https://github.com/thuml/Autoformer
https://github.com/ant-research/Pyraformer
https://github.com/MAZiqing/FEDformer
https://github.com/yuqinie98/PatchTST