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arXiv Machine Learning By Yuxuan Shu, Vasileios Lampos

Model-agnostic predictability quantification of multivariable time series

Read the original on arXiv Machine Learning →

The paper introduces the Multivariable Predictability Score (MAPS), a model‑agnostic metric designed to quantify the intrinsic predictability of multivariable time‑series forecasting tasks. MAPS leverages covariate information across various lookback windows and forecasting horizons to assess predictive information, addressing limitations of existing measures that often miss nonlinear dependencies or focus on univariate series. Experiments on synthetic and real‑world datasets show MAPS aligns closely with expected reference predictability (r = 0.963) and provides more reliable projections than six baseline measures.

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