Exogenous data is believed to play a key role for increasing forecasting accuracy. For an appropriate selection, a throughout relevance analysis is a fundamental first step, starting from the exogenous data similarity with the reference time series. Inspired by existing metrics for time series similarity, we introduce a new approach named FARM - Forward Angular Relevance Measure, able to effectively deal with real-time data streams. Our forward method relies on an angular feature 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 measure. 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 both synthetic but representative signals and real-world time series recordings. While demonstrating the improved capabilities with respect to existing approaches, we also discuss existing constraints and limitations of our idea.

翻译：外生数据被认为在提高预测精度方面发挥着关键作用。为进行恰当的选择，全面的相关性分析是基础的第一步，始于外生数据与参考时间序列的相似性。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（前向角相关性度量）的新方法，能够有效处理实时数据流。我们的前向方法依赖于一种角度特征，通过比较后续数据点的变化来高效地对齐时间扭曲序列。所提出的算法结合了局部与全局度量，以提供平衡的相关性度量。这使得部分、中间匹配也能被视为外生数据序列显著性的相关指标。作为初步验证，我们将FARM方法应用于合成但具有代表性的信号以及真实世界时间序列记录。在展示该方法相较于现有方法的改进能力的同时，我们也讨论了现有约束与局限性。