This repository is the official implementation of D-PAD: Deep-Shallow Multi-Frequency Patterns Disentangling for Time Series Forecasting.
To install requirements:
pip install -r requirements.txt
To train the model(s) in the paper, run this command:
sh ./scripts/run_ECL.sh
sh ./scripts/run_ETTh1.sh
sh ./scripts/run_ETTh2.sh
sh ./scripts/run_ETTm1.sh
sh ./scripts/run_ETTm2.sh
sh ./scripts/run_traffic.sh
sh ./scripts/run_weather.sh
The code directory structure is shown as follows:
D-PAD
├── datasets # seven datasets files
│ └── long
│ ├── ETTh1.csv
│ ├── ETTh2.csv
│ ├── ETTm1.csv
│ ├── ETTm2.csv
│ ├── ECL.csv
│ ├── traffic.csv
│ └── weather.csv
├── data_process # data loading and preprocessing code
│ └── ETT_data_loader.py
├── experiments # training, validation, and test code of D-PAD
│ ├── exp_basic.py
│ └── exp_ETT.py
├── layers
│ ├── Attention.py
│ ├── GAT.py
│ ├── GCN.py
│ ├── MCD.py
│ └── SEBlock.py
├── model # LaST main code
│ ├── D_PAD_adpGCN.py
│ ├── D_PAD_ATT.py
│ ├── D_PAD_GAT.py
│ └── D_PAD_SEBlock.py
└── utils
│ ├── ETTh_metrics.py
│ ├── gumbel_softmax.py
│ ├── math_utils.py
│ ├── metrics.py
│ └── tools.py
├── run_long.py # original version (multi-card)
├── singlecard.py # single card version
├── LICENSE # code license
└── README.md # This file