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.

本文引入一种名为FARM的新方法，通过角度测量比较时间序列的变化来对外生数据的相似性进行度量，以提高预测准确性，并通过丰富的实验来证明其有效性。

预测中的外部数据：FARM——一种相关性评估方法