New Rollcast method offers adaptive probabilistic time-series forecasting

A new probabilistic forecasting method called Rollcast has been introduced, designed for univariate time series. It utilizes a collection of rolling statistical anchors, such as means, medians, and quantiles, to define forecast locations and represent the current state. A learned gate assigns probabilities to these anchors, and residual distributions from similar historical states help quantify local uncertainty. The method was evaluated against an oracle simulator across various data-generating processes, showing competitive empirical coverage but with wider predictive intervals and higher CRPS than the oracle. AI

IMPACT Introduces a novel approach to time-series forecasting that could improve accuracy and interpretability in statistical modeling.

RANK_REASON The item is a research paper submitted to arXiv detailing a new forecasting method. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

• alphaXiv
• arXiv
• CatalyzeX Code Finder for Papers
• CORE Recommender
• DagsHub
• Giancarlo Vercellino
• Gotit.pub
• Hugging Face
• Influence Flower
• Rollcast
• ScienceCast

• paper
• other

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →