Adaptive Regime-Switching Forecasts with Distribution-Free Uncertainty: Deep Switching State-Space Models Meet Conformal Prediction

TL;DR

This paper introduces a novel approach combining deep switching state-space models with conformal inference to produce reliable, distribution-free uncertainty estimates in nonstationary regime-switching time series forecasting.

Contribution

It develops a unified conformal wrapper for various strong sequence models, providing finite-sample guarantees under nonstationarity and model misspecification.

Findings

Conformalized forecasters achieve near-nominal coverage.

The approach improves band efficiency while maintaining accuracy.

Effective on both synthetic and real datasets.

Abstract

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.