This repo is the official implementation for the paper: Wavelet-based Disentangled Adaptive Normalization for Non-stationary Times Series Forecasting

Figure 1. Overall structure of WDAN.

1. Install Pytorch and necessary dependencies.

pip install -r requirements.txt

2. Prepare Data. You can obtain the datasets from https://github.com/thuml/Time-Series-Library. Place the downloaded data in the folder ./dataset. The default data path configurations are recorded in ./utils/config_utils.py.

3. Train and evaluate the model (WDAN_backbone)

# Simple run
python run.py --model WDAN_iTransformer --dataset ETTh1

# Scripts for WDAN_iTransformer
bash ./scripts/WDAN_iTransformer.sh

We appreciate the following github repos a lot for their valuable code base or datasets:

https://github.com/thuml/Time-Series-Library

https://github.com/thuml/iTransformer

https://github.com/Thinklab-SJTU/Crossformer

https://github.com/MAZiqing/FEDformer

https://github.com/zhouhaoyi/Informer2020