mlsanityData to 14 Oct 2026

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

arXiv 2512.03298 ↗First posted 2 Dec 2025cs.LG cs.AIcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 4 of its 6 references that are in the map. First posted on arXiv 2 Dec 2025.

Builds on

4 of 6 references.

References · 4

  1. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,448
  2. 2021Efficiently Modeling Long Sequences with Structured State Spacescited by 2,465
  3. 2022On the Parameterization and Initialization of Diagonal State Space Modelscited by 424
  4. 2021Deep Switching State Space Model (DS3M) for Nonlinear Time Series Forecasting with Regime Switchingcited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Do Time Series Foundation Model Benchmarks Hide Regime-Dependent Failures? Evidence from Traffic Speed Forecastingcited by 5
  2. 2026Robust Transformer-Based One-Step Stock Index Forecasting via Shifted Data Augmentationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026DeRegiME: Deep Regime Mixtures for Probabilistic Forecasting under Distribution Shift
  2. 2025Conformal Prediction Beyond the Horizon: Distribution-Free Inference for Policy Evaluation
  3. 2026When Denoising Hurts: Rethinking the Terminal Step of Diffusion Time Series Forecasters -- Extended Version
  4. 2026Quantile-Guided Diffusion for Probabilistic Time-Series Forecasting
  5. 2026More Experts, Worse Dynamics: Inverse Scaling and Spectral Bias in Mixture-of-Experts State-Space Models
  6. 2026Diffusion-Driven State Space Models
  7. 2026Closing the Loop: A Control-Theoretic Framework for Provably Stable Time Series Forecasting with LLMs
  8. 2026A Decomposition-based State Space Model for Multivariate Time-Series Forecasting