Diversify: A General Framework for Time Series Out-of-Distribution Detection and Generalization

Time series remains one of the most challenging modalities in machine learning research. Out-of-distribution (OOD) detection and generalization on time series often face difficulties due to their non-stationary nature, wherein the distribution changes over time. The dynamic distributions within time series present significant challenges for existing algorithms, especially in identifying invariant distributions, as most focus on scenarios where domain information is provided as prior knowledge. This paper aims to address the issues induced by non-stationarity in time series through the exploration of subdomains within a complete dataset for generalized representation learning. We propose Diversify , a general framework, for OOD detection and generalization on dynamic distributions of time series. Diversify operates through an iterative process: first identifying the 'worst-case' latent distribution scenario, then working to minimize the gaps between these latent distributions. We implement Diversify by combining existing OOD detection methods according to either extracted features or outputs of models for detection while we also directly utilize outputs for classification. Theoretical insights support the framework's validity. Extensive experiments are conducted on seven datasets with different OOD settings across gesture recognition, speech commands recognition, wearable stress and affect detection, and sensor-based human activity recognition. Qualitative and quantitative results demonstrate that Diversify learns more generalized features and significantly outperforms other baselines.