Modular Bayesian Foundation Models: Bayesian time series encoders for OOD detection, routing and forecasting

arXiv:2610.39043v1 Announce Type: new Abstract: Industrial systems continuously generate time series that vary in sampling frequency, scale, dynamics, and operating regime. Consequently a single time-series foundation model may not be able to represent all regimes effectively and retraining a large model for every new dataset or regime shift is computationally expensive. Modular foundation models address this issue by reusing specialised encoders trained on diverse datasets and by allowing the expansion of capabilities through the addition of new experts. This approach requires compact experts