Streaming Time Series Subsequence Anomaly Detection: A Glance and Focus Approach

Subsequence anomaly detection for time series is a crucial problem in various real-world applications. However, existing methods proposed so far design the anomaly score functions solely based on either local neighborhood or global patterns, leading to unsatisfactory detection accuracy. In addition, these methods either cannot adapt, or yield insufficient accuracy and efficiency in streaming scenario. Therefore, we propose Sirloin, an accurate and efficient streaming time series subsequence anomaly detection framework. First, Sirloin proposes a glance and focus anomaly score function that takes both global and local information into consideration, contributing to an accurate anomaly detection. Second, Sirloin dynamically maintains an inverted file index and product quantization codebooks to index and compress the subsequences, hence is able to cope with the time series evolution and to process streaming batches efficiently. In addition, a dual index optimization strategy is put forward that further improves the efficiency. An experimental study in 11 different datasets from 5 domains offers insight into the performance of Sirloin, showing that it improves throughput on average 4x and enhances accuracy 58.02% compared to the state-of-the-art streaming method.