Evaluating the relevance of an exogenous data series is the first step in improving the prediction capabilities of a forecast algorithm. Inspired by existing metrics for time series similarity, we introduce a new approach named FARM - Forward Aligned Relevance Metric. Our forward method relies on an angular measure that compares changes in subsequent data points to align time-warped series in an efficient way. The proposed algorithm combines local and global measures to provide a balanced relevance metric. This results in considering also partial, intermediate matches as relevant indicators for exogenous data series significance. As a first validation step, we present the application of our FARM approach to synthetic but representative signals. While demonstrating the improved capabilities with respect to existing approaches, we also discuss existing constraints and limitations of our idea.

翻译：评估外生数据序列的相关性是提升预测算法能力的首要步骤。受现有时间序列相似性度量方法的启发，我们提出了一种名为FARM（前向对齐相关性度量）的新方法。该前向方法基于角度度量，通过比较后续数据点的变化，以高效方式对齐时间扭曲序列。所提出的算法结合局部与全局度量，提供平衡的相关性指标。这导致将部分、中间匹配也视为外生数据序列重要性的相关指示。作为初步验证步骤，我们展示了FARM方法在合成但具有代表性的信号上的应用。在展示其相较于现有方法优越性能的同时，也讨论了该思路的现有约束与局限性。