DESA: Discrete Event-Sparse Activation for Lightweight Multivariate Time-Series Forecasting

Recent advances in multivariate time-series forecasting have primarily focused on improving predictive accuracy. However, these improvements often come at the cost of substantially increased computation, limiting the applicability of such models in resource-constrained industrial environments. In this study, drawing on the event-centric perspective of Discrete Event System Specification (DEVS), we propose DESA (Discrete Event-Sparse Activation), which combines a low-cost base forecast with selective nonlinear correction. The Inertia Block processes the entire input sequence at low computational cost for every forecast, while the Event Block corrects the base forecast using local windows centered at selected time steps. Connecting the two blocks, the Surprise Gate uses reconstruction residuals from the Inertia Block and temporal differences to guide time-step selection and modulate the strength of the Event Block’s input. The Event Block gathers the selected time steps for processing and skips its core computations at unselected time steps. To learn this selective processing, we first train a reference model with the same architecture, applying the Event Block at every time step. We then use its predictions and intermediate signals as supervision to train a sparse model that performs selective event processing. Experimental results show that the sparse model achieves an average forecasting error close to that of the reference model while reducing the computational cost of inference. Overall, DESA lies on the empirical forecasting-error–FLOPs Pareto frontier among the compared models, providing a favorable trade-off between forecasting accuracy and computational cost.