Dance Steps : A Statistical Quantification of Data Pair Similarities

Herein we attempt to quantify Tom McClellan’s notion of Dance Steps between two series of data, often of differing time periods (a current, or dependent and a previous, or independent period data series), looking for similarities in the turning points to use as a reference for the future turning points and direction of the current period data series. The visual recognition of dance steps requires a fuzzy relationship between the time difference and degree differences of turning points that loosely correspond in time. We discuss a means of measuring this on the same scale as correlation where perfect matching equates to a metric of +1, no match to 0, and perfect inverse matching to-1