Speaker
Description
This work introduces a new interpretable time series foundation model, and its application to anomaly prediction for sPHENIX - a recently commissioned detector at the RHIC facility at BNL. Our goal is to monitor the detector’s operational status, identify early warning signs, and predict potential anomalies. Despite diverse time series modeling techniques, existing models are black boxes and fail to provide insights and explanations about their representations. We present VQShape, a pre-trained, generalizable, and interpretable model for time-series representation learning and classification. By introducing a novel representation for time-series data, we forge a connection between the latent space of VQShape and shape-level features. Using vector quantization, VQShape encodes time series from diverse domains into a shared set of low-dimensional codes, each corresponding to an abstract shape in the time domain. codes, where each code can be represented as an abstracted shape in the time domain. This enables the model not only to predict anomalies but also to highlight important precursor patterns, offering explanations for its predictions.
