A shape-based adaptive segmentation of time-series using particle swarm optimization

The increasing size of large databases has motivated many researchers to develop methods to reduce the dimensionality of data so that their further analysis can be easier and faster. There are many techniques for time-series' dimensionality reduction; however, majority of them need an input by the user such as the number of segments. In this paper, the segmentation problem is analyzed from the optimization point of view. A new approach for time-series' segmentation based on Particle Swarm Optimization (PSO) is proposed which is highly adaptive to time-series' shape and shape-based characteristics. The proposed approach, called Adaptive Particle Swarm Optimization Segmentation (APSOS), is tested on various datasets to demonstrate its effectiveness and efficiency. Experiments are conducted to show that APSOS is independent of user input parameters and the results indicate that the proposed approach outperforms common methods used for the time-series segmentation.