  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.
