Cedar

Source code and supplementary materials for "S. Deng et al., 'Domain Generalization in Time Series Forecasting', ACM TKDD, 2024".

Contents of this repository

• Source code and datasets.
• Pipelines about how to run and get results.
• Visualization of some datasets.

Prerequisites

The code has been successfully tested in the following environment:

• Python 3.9.15
• PyTorch 1.7.1
• CUDA 10.2
• Numpy 1.23.5
• Pandas 1.5.3

Folder structure

- cedar-dg
    - algorithms # model python files
	- data # dataset folders
		- kaggle_favorita
		- stock
		- traffic
        - syn # synthetic datasets
	- experiments # store experiment settings and results
        - base_settings 
            - deepar.csv
            - ... (other setting files)
    - lib # evaluation files, etc
    - preprocess # generate synthetic datasets
    - data.py # data loader file
	- train_main.py
    - ... (other python files)

Getting Started

Prepare your code

Clone this repo:

git clone https://github.com/songgaojundeng/cedar-dg
cd cedar-dg

Create experiment folder and setting files

Choose one dataset from traffic, favorita_family, favorita_family_store, stock_vol, samemv_diffp30, samep_diffmv30, samepmv_difft30, samet_diffpmv30. Taking traffic as the dataset example, run the following commands:

cd experiments
mkdir traffic
cp base_settings/*.csv traffic

Train baselines in deepar, wavenet (base), adarnn, vrnn, [base]_dann, [base]_groupdro, [base]_mldg, [base]_fish

• Taking model deepar as the example, run the following command 2 times (at root directory).

python train_main.py traffic deepar.csv deepar # run 2 times

The first time: train under one seed and find the best parameter. The second time: train again under other seeds.

Train baseline [base]_mmd

• Step 1: Generate the optimal experimental settings from the base model deepar (64 is the batch size):

python gen_settings_from_base.py traffic deepar deepar_mmd deepar_mmd 64

• Step 2: Train the model deepar_mmd under different settings:

python train_main.py traffic deepar_mmd.csv deepar_mmd # run 2 times

Train Cedar [base]_cedar

• Step 1: Generate the optimal experimental settings from the base model deepar (64 is the batch size):

python gen_settings_from_base.py traffic deepar deepar_cedar deepar_cedar 64

• Step 2: Train the model deepar_cedar under different settings:

python train_main.py traffic deepar_cedar.csv deepar_cedar # run 2 times

Read results

• for Cedar

python get_seed_results_cedar.py traffic deepar_cedar.csv deepar_cedar

• for all other baselines

python get_seed_results_baseline.py traffic deepar.csv deepar

Train traditional time series models

python train_traditional.py traffic 0

Cite

Please cite our paper if you find this code useful for your research:

@article{10.1145/3643035,
author = {Deng, Songgaojun and Sprangers, Olivier and Li, Ming and Schelter, Sebastian and de Rijke, Maarten},
title = {Domain Generalization in Time Series Forecasting},
year = {2024},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
issn = {1556-4681},
url = {https://doi.org/10.1145/3643035},
doi = {10.1145/3643035},
journal = {ACM Trans. Knowl. Discov. Data},
month = {jan}
}