RainbowPillow / ModWaveMLP

[AAAI 2024] ModWaveMLP: MLP-Based Mode Decomposition and Wavelet Denoising Model to Defeat Complex Structures in Traffic Forecasting

Code for our paper: "[ ModWaveMLP: MLP-Based Mode Decomposition and Wavelet Denoising Model to Defeat Complex Structures in Traffic Forecasting]".

1. Table of Contents

data            ->  metr-la and pems-bay raw data and processed data
Datasets        ->  dataset preprocessing code
Model           ->  model implementation 

2. Requirements

pip install -r requirements.txt

more details: cudnn==8.2.1, cudatoolkit==11.3.1

3. Data Preparation

Alterbatively, the datasets can be found as follows:

• METR-LA and PEMS-BAY: These datasets were released by DCRNN[1]. Data can be found in its GitHub repository. Please put these two files metr-la.h5 and pems-bay.h5 in the data folder

4. Training the ModWaveMLP Model

The hyperparameters of ModWaveMLP can be changed in the Parameters.py

python run_MoDWaveMLP.py --dataset metr-la --horizon 12 --history_length 12
python run_MoDWaveMLP.py --dataset pems-bay --horizon 12 --history_length 12

Regarding the issue in the data preprocessing code, we are in the process of updating it and experimenting with it, and the new code and results will be updated.To prevent misunderstandings, we hide the data preprocessing code for now.

If you have any questions or suggestions, please feel free to contact us through this e-mail address([e-mail]), we sincerely look forward to communicating with you!