TinyCycle: Time-Series Forecasting in 30 Parameters with Closed-Form Fitting

In long-term time-series forecasting, lightweight models can compare to those of complex models, but aggregate metrics reveal little about the information these models recover or the sources of their performance differences. Based on phase recurrence across cycles, we decompose both the true future and model predictions into Structure and Dynamics. We then use orthogonality to decompose MSE. Diagnostics across multiple models show that models recover Structure substantially better than Dynamics, and that modeling Structure can account for most of their predictions. Motivated by this observation, we propose TinyCycle, which directly models the Structure of the data without modeling complex temporal mappings. Across experiments, TinyCycle recovers most of the forecasting performance of mainstream time-series forecasting models with no more than 31 parameters using closed-form fitting.