Graph Neural Networks for Multivariate Time Series Regression with Application to Seismic Data

TL;DR

This paper introduces TISER-GCN, a graph neural network designed for multivariate time series regression, effectively leveraging spatial and temporal data to improve seismic ground shaking predictions, outperforming baselines with less input data.

Contribution

The paper presents a novel GNN architecture tailored for long multivariate time series regression tasks, especially in seismic data analysis, addressing limitations of existing methods.

Findings

16.3% average MSE reduction compared to baselines

Achieves similar accuracy with half the input size

Effective on seismic earthquake waveform datasets

Abstract

Machine learning, with its advances in deep learning has shown great potential in analyzing time series. In many scenarios, however, additional information that can potentially improve the predictions is available. This is crucial for data that arise from e.g., sensor networks that contain information about sensor locations. Then, such spatial information can be exploited by modeling it via graph structures, along with the sequential (time series) information. Recent advances in adapting deep learning to graphs have shown potential in various tasks. However, these methods have not been adapted for time series tasks to a great extent. Most attempts have essentially consolidated around time series forecasting with small sequence lengths. Generally, these architectures are not well suited for regression or classification tasks where the value to be predicted is not strictly depending on the most recent values, but rather on the whole length of the time series. We propose TISER-GCN, a novel graph neural network architecture for processing, in particular, these long time series in a multivariate regression task. Our proposed model is tested on two seismic datasets containing earthquake waveforms, where the goal is to predict maximum intensity measurements of ground shaking at each seismic station. Our findings demonstrate promising results of our approach -- with an average MSE reduction of 16.3% - compared to the best performing baselines. In addition, our approach matches the baseline scores by needing only half the input size. The results are discussed in depth with an additional ablation study.