AI RESEARCH
Convolutionally Low-Rank Models with Modified Quantile Regression for Interval Time Series Forecasting
arXiv CS.LG
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ArXi:2604.15791v1 Announce Type: new The quantification of uncertainty in prediction models is crucial for reliable decision-making, yet remains a significant challenge. Interval time series forecasting offers a principled solution to this problem by providing prediction intervals (PIs), which indicates the probability that the true value falls within the predicted range.