Time Series Segmentation through Automatic Feature Learning
Jorge Ortiz · 2018
Traditional changepoint detection methods look for boundaries that are defined as abrupt variations in the generative parameters of a data sequence. However, we observe that breakpoints – human-specified boundaries – occur on more subtle boundaries that are non-trivial to detect with these statistical methods. In this work, we propose a new semi-supervised approach, based on deep learning, that outperforms existing techniques and learns the more subtle breakpoint boundaries with a high accuracy. Through extensive experiments on various real world data sets – including human-activity sensing data, speech signals, and electroencephalogram (EEG) activity traces – we demonstrate the effectiveness of our algorithm for practical applications. Furthermore, we show that our approach achieves significantly better performance than previous methods.