Time-Series Classification for Dynamic Strategies in Multi-Step Forecasting
CoRR(2024)
摘要
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
time, with an average reduction in mean-squared error of 11
typically triples the top-1 accuracy compared to current approaches. Notably,
we show DyStrat generalises well for any MSF task.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要