When and Where to Transfer: Learning Transferable Historical Contexts for Short-History Multivariate Time Series Forecasting

Related sources can improve multivariate time series forecasting when the target history is short, but their usefulness varies across source groups and historical periods. A key challenge is to identify informative historical contexts from the sources. This paper proposes Hierarchical Context Transfer (HiCT), a framework that adaptively weights source groups and their historical contexts to determine both where and when to transfer. A shared context encoder and cross-attention are used to learn weights over source groups and historical contexts. These weights guide regularized transfer estimation, followed by an optional target-specific correction. This paper establishes a target excess-risk bound for the context-weighted transfer estimator. Experiments demonstrate competitive predictive performance across four multivariate forecasting datasets and a favorable accuracy–runtime trade-off against the evaluated baselines. Source-corruption ablations illustrate the roles of group-level and within-group weighting in adapting to unreliable source histories.