Mining time series data for segmentation by using Ant Colony Optimization

Abstract

In trying to distinguish data features within time series data for specific time intervals, time series segmentation technology is often required. This research divides time series data into segments of varying lengths. A time series segmentation algorithm based on the Ant Colony Optimization (ACO) algorithm is proposed to exhibit the changeability of the time series data. In order to verify the effect of the proposed algorithm, we experiment with the Bottom-Up method, which has been reported in available literature to give good results for time series segmentation. Simulation data and genuine stock price data are also used in some of our experiments. The research result shows that time series segmentation run by the ACO algorithm not only automatically identifies the number of segments, but its segmentation cost was lower than that of the time series segmentation using the Bottom-Up method. More importantly, during the ACO algorithm process, the degree of data loss is also less compared to that of the Bottom-Up method.