Spatial-Temporal Identity: A Simple yet Effective Baseline for Multivariate Time Series Forecasting

Code for our CIKM'22 short paper: "Spatial-Temporal Identity: A Simple yet Effective Baseline for Multivariate Time Series Forecasting".

Our code is built on BasicTS, an open-source standard time series forecasting benchmark. You can also find STID in BasicTS.

We recommend you use BasicTS to find more baselines and more detailed comparisons.

Multivariate Time Series (MTS) forecasting plays a vital role in a wide range of applications. Recently, Spatial-Temporal Graph Neural Networks (STGNNs) have become increasingly popular MTS forecasting methods due to their state-of-the-art performance. However, recent STGNN-based methods are becoming more sophisticated with limited performance improvements. This phenomenon motivates us to explore the critical factors of MTS forecasting and design a model that is as powerful as STGNNs, but more concise and efficient. In this paper, we identify the indistinguishability of samples in both spatial and temporal dimensions as a key bottleneck, and propose a simple yet effective baseline for MTS forecasting by attaching Spatial and Temporal IDentity information (STID). STID achieves the best performance and efficiency simultaneously based on simple multi-layer perceptrons (MLPs). These results suggest that by solving the indistinguishability of samples, we can design models more freely, without being limited to STGNNs.

📚 Table of Contents

basicts   --> The BasicTS, which provides standard pipelines for training MTS forecasting models. Don't worry if you don't know it, because it doesn't prevent you from understanding STID's code.

datasets  --> Raw datasets and preprocessed data

figures   --> Some figures used in README.

scripts   --> Data preprocessing scripts.

stid/stid_arch      --> The implementation of STID.

stid/STID_${DATASET_NAME}.py    --> Training configs.

Replace ${DATASET_NAME} with one of PEMS03, PEMS04, PEMS07, PEMS08, METR-LA, and PEMS-BAY.

💿Requirements

The code is built based on Python 3.9, PyTorch 1.10.0, and EasyTorch. You can install PyTorch following the instruction in PyTorch. For example:

pip install torch==1.10.0+cu111 torchvision==0.11.0+cu111 torchaudio==0.10.0 -f https://download.pytorch.org/whl/torch_stable.html

After ensuring that PyTorch is installed correctly, you can install other dependencies via:

pip install -r requirements.txt

📦 Data Preparation

Download Data

You can download all the raw datasets at Google Drive or Baidu Yun(password: 6v0a), and unzip them to datasets/raw_data/.

Data Preprocessing

cd /path/to/your/project
python scripts/data_preparation/${DATASET_NAME}/generate_training_data.py

Replace ${DATASET_NAME} with one of METR-LA, PEMS-BAY, PEMS03, PEMS04, PEMS07, PEMS08, or any other supported dataset. The processed data will be placed in datasets/${DATASET_NAME}.

Or you can pre-process all datasets by.

cd /path/to/your/project
bash scripts/data_preparation/all.sh

🎯 Train STID

python stid/run.py --cfg stid/STID_${DATASET_NAME}.py --gpus '0'

Replace ${DATASET_NAME} with one of PEMS03, PEMS04, PEMS07, PEMS08, METR-LA, and PEMS-BAY, e.g.,

python stid/run.py --cfg stid/STID_PEMS04.py --gpus '0'

📈 Experiment Results

🔗 More Related Works

• D2STGNN: Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting. VLDB'22.

• STEP: Pre-training-Enhanced Spatial-Temporal Graph Neural Network For Multivariate Time Series Forecasting. KDD'22.

• BasicTS: An Open Source Standard Time Series Forecasting Benchmark.

Citing

@inproceedings{10.1145/3511808.3557702,
author = {Shao, Zezhi and Zhang, Zhao and Wang, Fei and Wei, Wei and Xu, Yongjun},
title = {Spatial-Temporal Identity: A Simple yet Effective Baseline for Multivariate Time Series Forecasting},
year = {2022},
booktitle = {Proceedings of the 31st ACM International Conference on Information & Knowledge Management},
pages = {4454–4458},
location = {Atlanta, GA, USA}
}