PRISM: Lightweight Long-Term Time Series Forecasting with Period-Aligned Summarization

Long-term time series forecasting (LTSF) requires models that capture temporal dependencies while remaining computationally efficient. We propose PRISM, a lightweight forecasting architecture that exploits periodic structure as a computational prior. PRISM reorganizes the input sequence into a period-aligned representation with cross-period and intra-period axes, and assigns asymmetric computational roles to them. Sequence summarization compresses the cross-period dimension before forecasting, while lightweight temporal mixing refines the intra-period structure. This allows the forecasting head to operate on a compact representation rather than the full historical sequence. Experiments on seven LTSF benchmarks across four forecasting horizons show that PRISM maintains competitive forecasting accuracy with a small computational footprint. Efficiency measurements and structural ablations further confirm that the period-aligned design substantially reduces computational cost while preserving predictive performance. Overall, PRISM provides a simple and effective approach to balancing forecasting accuracy and computational efficiency in LTSF.