Fusion-OSR: Cross-Domain Contrastive Learning with Weibull Calibration for Time Series Open Set Recognition

Abstract

In recent years, numerous Time Series Anomaly Detection methods have emerged, focusing primarily on detecting anomalies within time series, with limited work on open set recognition. In real-world scenarios, obtaining anomaly data is challenging. We can acquire a limited number of samples representing unknown anomaly classes, but these samples are far from representing the full distribution of all possible unknown classes. A direct approach would be to learn from known samples to achieve the goal of recognizing unknown samples. This paper contributes twofold: First, we introduce a cross-domain approach combining time-domain and frequency-domain information for time series Open Set Recognition, achieving superior detection under limited samples. This is the first application of a crossdomain method in the field of time series Open Set Recognition. Second, we propose a learning scheme using the Cross-domain Weibull distribution for classifier calibration. The proposed method, Fusion-OSR, achieves state-of-the-art accuracy, F1score, and recall across 30 datasets.