Time-Series Classification for Dynamic Strategies in Multi-Step
  Forecasting
Riku Green Grant Stevens Telmo de Menezes e Silva Filho Zahraa Abdallah
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.