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.

翻译：时间序列中的多步预测（MSF）是指预测未来多个时间步长的能力，这几乎是所有时间领域的基础。要进行此类预测，必须假设时间动态的递归复杂性。此类假设被称为用于训练预测模型的预测策略。先前的研究表明，在未知数据上评估之前，无法预先确定哪种预测策略是最优的。此外，当前的MSF方法采用单一（固定）预测策略。本文描述了最优预测策略在实例层面的差异性，并提出了面向MSF的动态策略（DyStrat）。我们使用来自不同尺度、领域和多步预测时间长度的10个数据集进行实验。当使用基于随机森林的分类器时，DyStrat在94%的情况下优于最优固定策略（该策略预先未知），平均均方误差降低11%。与当前方法相比，我们的方法通常将前1准确率提高三倍。值得注意的是，我们表明DyStrat对任何MSF任务都具有良好的泛化能力。