Forecasting Multivariate Time Series under Predictive Heterogeneity: A Validation-Driven Clustering Framework
Ziling Ma; Ángel López Oriona; Hernando Ombao; Ying Sun · 2026 · arXiv
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Abstract (excerpt)
We study adaptive pooling under predictive heterogeneity in high-dimensional multivariate time series forecasting, where global models improve statistical efficiency but may fail to capture heterogeneous predictive structure, while naive specialization can induce negative transfer. We formulate adaptive pooling as a statistical decision problem and propose a validation-driven framework that determines when and how specialization should be applied. Rather than grouping series based on representation similarity, we define partitions through out-of-sample predictive performance, thereby aligning
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Metadata source: arXiv