Adaptive Regime-Switching Forecasts with Distribution-Free Uncertainty: Deep Switching State-Space Models Meet Conformal Prediction
This addresses the challenge of reliable uncertainty quantification in nonstationary time series forecasting, which is crucial for applications like finance and climate modeling, though it builds incrementally on existing conformal prediction methods.
The paper tackles the problem of providing calibrated uncertainty for regime-switching time series forecasting by combining Deep Switching State Space Models with Adaptive Conformal Inference, achieving near-nominal coverage with competitive accuracy across synthetic and real datasets.
Regime transitions routinely break stationarity in time series, making calibrated uncertainty as important as point accuracy. We study distribution-free uncertainty for regime-switching forecasting by coupling Deep Switching State Space Models with Adaptive Conformal Inference (ACI) and its aggregated variant (AgACI). We also introduce a unified conformal wrapper that sits atop strong sequence baselines including S4, MC-Dropout GRU, sparse Gaussian processes, and a change-point local model to produce online predictive bands with finite-sample marginal guarantees under nonstationarity and model misspecification. Across synthetic and real datasets, conformalized forecasters achieve near-nominal coverage with competitive accuracy and generally improved band efficiency.