Dynamic discretization of continuous values from time series

Abstract

Two methods to assign discrete values to continuous values from time series, using dynamic information about the series, are proposed. The first method is based on a particular statistic which allows us to select a discrete value for a new continuous value from the series. The second one is based on a concept of significant distance between consecutive values from time series which is defined. This definition is based on qualitative changes in the time series values. In both methods, the conversion process of continuous values into discrete values is dynamic in opposition to static classical methods used in machine learning. Finally, we use the proposed methods in a practical case. We transform the daily clearness index time series into discrete values. The results display that the series with discrete values obtained from the dynamic process captures better the sequential properties of the original continuous series.