Path Signatures are Unsupervised Time Series Anomaly Extractors

Abstract

Time Series Anomaly Detection (TSAD) methods often require large datasets and extensive domain knowledge, limiting their effectiveness in data-scarce environments. We introduce a novel unsupervised TSAD approach based on path signatures, which efficiently extracts shape features from time series data without requiring additional parameters. By leveraging multi-scale signature feature extraction, our method achieves faster inference and higher performance compared to deep learning models, as demonstrated on multiple cross-domain datasets. Experimental results confirm the strong generalization capability and computational efficiency of our approach.