Speeding Up Multivariate Time Series Segmentation Using Feature Extraction

Multivariate time series segmentation (also known as multiple change-point detection), is one of the important basic tasks in time series data mining. A time series is an ordered sequence of elements. A segment is a region of the time series where the observations are similar according to some measure. The aim of time series segmentation is to identify these regions. In the field of wearable device application, time series segmentation is applied to behavior switching discovering, activity recognition, dimension reduction and so on. With the widespread use of wearable devices, large amount of sensor data can be available. To effectively utilize this time series data, it is important to segment the data into homogeneous phases prior to build an appropriate model for the task, and it must be done at large scale. In this paper, a high accuracy and low time complexity time series segmentation method is proposed based on the time series feature extraction and segmented gaussian model, and applies it to the human activity data sets to discover activity switching.