Exogenous Data in Forecasting: FARM -- A New Measure for Relevance Evaluation
This work addresses the challenge of selecting useful external data for forecast algorithms, but it is incremental as it builds on existing time series similarity metrics.
The paper tackles the problem of evaluating the relevance of exogenous data series for forecasting by introducing FARM, a new metric that combines local and global measures to identify partial matches, and demonstrates its improved capabilities on synthetic signals.
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.