ARISE: Retrieval-Augmented Forecasting for Irregular Time Series

Retrieval-augmented generation (RAG) supports time series forecasting by reusing historical windows and their subsequent observations. However, most existing methods are designed for regularly sampled data and face two challenges when extended to irregular multivariate time series (IMTS). Uneven observation density makes fixed windows prone to insufficient information or redundancy, while irregular sampling and asynchronous observations complicate the retrieval of relevant historical cases. To address these challenges, we propose ARISE, a retrieval-augmented forecasting framework for IMTS. First, ARISE introduces a dynamic window construction mechanism that learns a sample-specific left boundary for each historical window under multiple training objectives while keeping its right boundary fixed, yielding compact history–future pairs with predictive value. Second, a multi-evidence knowledge retrieval mechanism assesses historical relevance from three perspectives: observation patterns, temporal dynamics, and value information. By combining multi-view candidate retrieval with maximal marginal relevance (MMR) selection, ARISE balances relevance and diversity to retrieve complementary historical evidence. A retrieval-based memory fusion module then uses cross-attention to incorporate the retrieved histories and their observed continuations into the predictor's hidden representations. Finally, a forecasting decoder produces the final prediction. Experimental results on multiple real-world datasets show that ARISE outperforms existing state-of-the-art methods in forecasting accuracy.