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

🔥 [New Results] We added the performance of STID on large scale MTS datasets.

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

Caution

STID is built on BasicTS, an open-source benchmark for time series forecasting. We highly recommend reproducing STID and other MTS forecasting models on any dataset using BasicTS. This repository will not be updated frequently; instead, updates will be made in BasicTS.

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

experiments  --> Training scripts.

figures   --> Some figures used in README.

scripts   --> Data preprocessing scripts.

stid/arch      --> The implementation of STID.

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

Replace ${DATASET_NAME} with one of PEMS03, PEMS04, PEMS07, PEMS08, METR-LA, PEMS-BAY, or any other dataset you want to use.

💿Requirements

The code is built with BasicTS, you can easily install the requirements by (take Python 3.11 + PyTorch 2.3.1 + CUDA 12.1 as an example):

# Install Python
conda create -n BasicTS python=3.11
conda activate BasicTS
# Install PyTorch
pip install torch==2.3.1 torchvision==0.18.1 torchaudio==2.3.1 --index-url https://download.pytorch.org/whl/cu121
# Install other dependencies
pip install -r requirements.txt

More details can be found in BasicTS.

📦 Data Preparation

Download Data

You can download the all_data.zip file from Google Drive or Baidu Netdisk. Unzip the files to the datasets/ directory:

cd /path/to/STID # not STID/stid
unzip /path/to/all_data.zip -d datasets/

These datasets have been preprocessed and are ready for use.

🎯 Train STID

python experiments/train.py --cfg stid/${DATASET_NAME}.py --gpus '0'

Replace ${DATASET_NAME} with one of PEMS03, PEMS04, PEMS07, PEMS08, METR-LA, PEMS-BAY, or any other dataset you want to use.

python experiments/train.py --cfg 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: A Fair and Scalable Time Series Forecasting Benchmark and Toolkit.

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}
}