  Assessing the predictive power of both data and models holds paramount
significance in time-series machine learning applications. Yet, preparing time
series data accurately and employing an appropriate measure for predictive
power seems to be a non-trivial task. This work involves reviewing and
establishing the groundwork for a comprehensive analysis of shaping time-series
data and evaluating various measures of dependence. Lastly, we present a
method, framework, and a concrete example for selecting and evaluating a
suitable measure of dependence.
