Mining an optimal prototype from a periodic time series: An evolutionary computation-based approach

The mining of meaningful shapes of time series is done widely in order to find shapes that can be used, for example, in classification problems or in summarizing signals. Normally, shapes that summarize periodic signals have to be mined visually, and in order to find a shape of high quality, several tests haves to be made. This makes visual mining slow and sometimes even frustrating. A method for summarizing a periodic time series automatically is presented in this study. The method is based on evolutionary computation and the results show that by using it, shapes can be found that summarize a time series better than shapes found using visual mining.