Unifying Global and Local Anomaly Detection for Time Series

Anomaly detection for time series data has been proven useful from both academia and industry, and can be categorized into global and local methodsbased on the range of data used to determine anomalies. To the best of our knowledge, there are no methods that unify the global and local methods together.In this study, we propose an approach that unifies the global and local anomaly detection to achieve the good performance. To make the unification possible, wediscretize time series data and partition the discrete data into global and local anomaly detection parts based on a concept of local factor. We design a rule-based global method using non-redundant association rules with tolerances to handle the difficulty of the latency phenomenon and the excessive number ofrules in time series data. We develop a Transformer based on local method with LSH (Locality Sensitive Hashing) Attention to fit better for discrete data. Wefinally conduct an extensive experimental study to verify the effectiveness and efficiency of our approach.