PeakTS: Forecasting High-Value Regions via Risk-Conditioned Distribution Correction

Time-series forecasting models often achieve strong average performance yet remain unreliable in critical high-value regions, where rare large values are particularly difficult to predict accurately. A key limitation is that existing methods typically lack explicit mechanisms for modeling historical patterns associated with future high-value outcomes. To address this issue, we propose a probability-distribution correction framework, PeakTS, for high-value time-series forecasting. PeakTS predicts an ordered categorical distribution over future value bins and derives risk-sensitive statistics, including the distribution mean and upper quantiles, to adaptively correct deterministic forecasts. We further introduce a Risk-Conditioned Peak Memory module that stores general and peak-precursor prototypes and retrieves relevant patterns from time-, difference-, and frequency-domain views to modulate the predictive distribution. To better learn sparsely represented high-value regions, we employ a tail-aware ordinal objective with frequency-based threshold reweighting and upper-tail weighting. Experiments on multiple forecasting benchmarks demonstrate consistent improvements in high-value forecasting while maintaining competitive overall forecasting performance.