STTS-EAD: Improving Spatio-Temporal Learning Based Time Series Prediction via

TL;DR

STTS-EAD is an innovative end-to-end approach that integrates anomaly detection with spatio-temporal forecasting, leveraging auxiliary information to enhance multivariate time series prediction accuracy.

Contribution

It introduces the first method to combine anomaly detection and forecasting in training, utilizing spatio-temporal data for improved prediction performance.

Findings

Outperforms baseline methods on stock and sales datasets.

Effectively processes anomalies to boost forecasting accuracy.

Demonstrates significant improvements in real-world applications.

Abstract

Handling anomalies is a critical preprocessing step in multivariate time series prediction. However, existing approaches that separate anomaly preprocessing from model training for multivariate time series prediction encounter significant limitations. Specifically, these methods fail to utilize auxiliary information crucial for identifying latent anomalies associated with spatiotemporal factors during the preprocessing stage. Instead, they rely solely on data distribution for anomaly detection, which can result in the incorrect processing of numerous samples that could otherwise contribute positively to model training. To address this, we propose STTS-EAD, an end-to-end method that seamlessly integrates anomaly detection into the training process of multivariate time series forecasting and aims to improve Spatio-Temporal learning based Time Series prediction via Embedded Anomaly Detection. Our proposed STTS-EAD leverages spatio-temporal information for forecasting and anomaly detection, with the two parts alternately executed and optimized for each other. To the best of our knowledge, STTS-EAD is the first to integrate anomaly detection and forecasting tasks in the training phase for improving the accuracy of multivariate time series forecasting. Extensive experiments on a public stock dataset and two real-world sales datasets from a renowned coffee chain enterprise show that our proposed method can effectively process detected anomalies in the training stage to improve forecasting performance in the inference stage and significantly outperform baselines.