Author

Jason B. Cho

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Preprint Nov 2026

dsp: An R Package for Bayesian Time Series Modeling with the Dynamic Shrinkage Process

dsp provides a unified state-space framework for modeling nonstationary time series using the Dynamic Shrinkage Process (DSP), a class of global-local shrinkage priors. DSP adaptively regularizes time-varying parameters to shrink noise while preserving abrupt changes and other local structure. The package supports Gaus...

Jason B. Cho, Daniel R. Kowal, Hao-Xuan Wu et al. · 0 citations

Preprint Oct 2026

Volatility-Corrected Prediction Intervals: Locally Adaptive Uncertainty Quantification for Probabilistic Time-Series Forecasting

Prediction intervals from probabilistic time-series forecasters are commonly assessed by marginal coverage, the long-run proportion of observations falling inside the intervals. However, forecast errors in time series are often serially dependent and heteroskedastic, so prediction intervals can attain near-nominal marg...

Jason B. Cho, David S. Matteson · 0 citations