PeriCal: Adaptively Calibrating Periodic Patterns for Time-Series Forecasting

Periodic patterns provide important temporal information in multivariate time-series forecasting by capturing regular variations in real-world systems. However, periodic patterns do not recur identically across contexts: their timing and magnitude may vary with evolving system states and external conditions. Existing methods typically model periodic patterns with fixed representations, which may become inconsistent with current observations when such variations occur. To address this limitation, we propose PeriCali, a lightweight forecasting framework that adapts periodic patterns through Adaptive Periodic Calibration. PeriCal learns input-conditioned calibration factors to adjust periodic patterns along both temporal and value dimensions, enabling the periodic representation to better match the current temporal context before being integrated with local observations through two-dimensional convolution. Experiments on 11 real-world benchmarks demonstrate that PeriCal achieves strong forecasting performance across datasets from multiple application domains. Further analysis verifies the effectiveness of the proposed adaptive periodic modeling approach.