TimeSelect: Variable-Wise Historical Context Selection for Time Series Forecasting

Most time series forecasting methods use a fixed-length historical window and implicitly assume that all observations are equally informative. In practice, however, the effective context varies across forecasting instances and variables, while distant observations may introduce outdated or irrelevant patterns. To address this issue, we propose TimeSelect, a forecasting framework that adaptively selects informative history and organizes it into a stable temporal representation. Before training, TimeSelect derives temporal bases from lagged correlations in the training data to capture stable dependencies across temporal positions. During forecasting, a lightweight selector estimates a variable-specific context length and constructs a differentiable mask to suppress less informative observations. The selected history is then projected onto the correlation-derived bases and processed by a learnable backbone to model inter-variable dependencies and predict the future trajectory. To preserve recent periodic variations that may be smoothed by global modeling, a period-aware refinement module further adjusts the initial forecast using gated residuals derived from the latest observed cycles. Extensive experiments on diverse real-world benchmarks demonstrate the consistent effectiveness of TimeSelect.