Curriculum Learning-Based Multivariate Time Series Anomaly Detection

Abstract

Anomaly detection in multivariate time series (MVTS) is a significant research domain across several industries, including cybersecurity and industrial systems. There have been several deep learning-based methods proposed within this research domain. The development of high capacity frameworks has received the majority of attention in recent years due to the availability of large open-source datasets and improvement in computer processing power. However, there has not been much attention in investigating the importance of how the data is presented to these techniques during training. Curriculum learning (CL), a technique based on ordered learning, was proposed for machine learning. In CL, the model first learns from easy data and is progressively trained with increasingly difficult data. In this paper, we propose data-based CL for MVTS anomaly detection. We further introduce the CL concept to the learner (model), in which we first train a simple model and then utilize a complex model in the final training round. To the best of our knowledge, we are the first to investigate these approaches in MVTS anomaly detection. We evaluate the proposed designs on the SWaT dataset using the F1 score and the results show an improvement in performance.