Time-Series Classification for Dynamic Strategies in Multi-Step Forecasting
This addresses the challenge of unknown optimal strategies in multi-step forecasting for temporal domains, offering a dynamic approach that improves accuracy, though it is incremental as it builds on existing strategy concepts.
The paper tackles the problem of selecting optimal forecasting strategies for multi-step time-series forecasting by proposing Dynamic Strategies (DyStrat), which outperforms the best fixed strategy 94% of the time with an average 11% reduction in mean-squared error and typically triples top-1 accuracy.
Multi-step forecasting (MSF) in time-series, the ability to make predictions multiple time steps into the future, is fundamental to almost all temporal domains. To make such forecasts, one must assume the recursive complexity of the temporal dynamics. Such assumptions are referred to as the forecasting strategy used to train a predictive model. Previous work shows that it is not clear which forecasting strategy is optimal a priori to evaluating on unseen data. Furthermore, current approaches to MSF use a single (fixed) forecasting strategy. In this paper, we characterise the instance-level variance of optimal forecasting strategies and propose Dynamic Strategies (DyStrat) for MSF. We experiment using 10 datasets from different scales, domains, and lengths of multi-step horizons. When using a random-forest-based classifier, DyStrat outperforms the best fixed strategy, which is not knowable a priori, 94% of the time, with an average reduction in mean-squared error of 11%. Our approach typically triples the top-1 accuracy compared to current approaches. Notably, we show DyStrat generalises well for any MSF task.