PKTime:Lightweight Time Series Forecasting via Kronecker-Structured Modeling with Adaptive Period Selection

Lightweight time-series forecasting requires compact models that capture temporal dependencies effectively. We propose PKTime, a lightweight forecasting model combining Kronecker-structured modeling with adaptive period selection. PKTime selects periods from training data and organizes historical observations into intra-period and inter-period dimensions. Each branch uses a diagonal-plus-low-rank mapping for intra-period interactions and a dense mapping for inter-period dependencies, yielding a compact Kronecker-separable predictor. Multiple branches and their fusion weights are jointly learned to capture different periodic structures, with parameters shared across variables. Period selection occurs only at initialization, and forecasting operates entirely in the time domain. Experiments on eight benchmark datasets demonstrate state-of-the-art forecasting performance. Source code is available at https://anonymous.4open.science/r/PKTime-CE08.