CHARM: Probabilistic Forecasting with Uncertain Historical Context

Under changing predictive dynamics, a forecaster must make predictions while the relevance of its historical context remains uncertain. Our key insight is to reuse learned predictive knowledge across multiple candidate contexts, maintaining uncertainty over which historical context should inform the next prediction. We introduce CHARM (Changepoint-Aware Recurrent Memory), which maintains a recurrent state for each candidate history under a shared, frozen predictor. Bayesian online changepoint detection updates run-length probabilities using predictive likelihoods, and CHARM combines the candidate predictive densities under these probabilities to account for uncertainty over context boundaries. We evaluate CHARM on financial realized volatility, where persistent dependence makes history valuable while changing dynamics make its relevance uncertain. Experiments show that CHARM improves average predictive log scores over the same forecaster with continuous history, with gains varying across markets.