The repo is the official implementation for the paper: "Amplifier: Bringing Attention to Neglected Low-Energy Components in Time Series Forecasting".
To get started, ensure you have Conda installed on your system and follow these steps to set up the environment:
conda create -n Amplifier python=3.8
conda activate Amplifier
pip install -r requirements.txt
All the datasets needed for Amplifier can be obtained from the Google Drive provided in Autoformer.
Create a separate folder named ./dataset and place all the CSV files in this directory.
Note: Place the CSV files directly into this directory, such as "./dataset/ETT-small/ETTh1.csv"
You can reproduce the experiment results as the following examples:
bash ./scripts/ETTm1.sh
bash ./scripts/ETTh1.sh
bash ./scripts/ECL.sh
We appreciate the following GitHub repositories for providing valuable code bases and datasets:
https://github.com/lss-1138/SparseTSF
https://github.com/plumprc/RTSF
https://github.com/cure-lab/LTSF-Linear
https://github.com/aikunyi/FreTS
https://github.com/VEWOXIC/FITS
https://github.com/chenzRG/Fredformer
https://github.com/thuml/iTransformer
https://github.com/yuqinie98/PatchTST
https://github.com/thuml/Nonstationary_Transformers