Temporal Convolutional Network-Based Time-Series Segmentation

Time-series segmentation is useful to identify the underlying characteristics of time series and summarize time series as a sequence of states. Partitioning time series into the states makes the complex time series easily understandable and interpretable. However, as labels for time points are not normally available, it is challenging to figure out the accurate segments and their states. Therefore, we propose an unsupervised time-series segmentation using the inherent properties in times series. The states can be characterized by diverse length patterns inherent in time series, and thus capturing diverse patterns is crucial in an unsupervised time-series segmentation. We adopt a temporal convolutional network (TCN) as our key component to learn diverse length patterns since the intermediate layers in TCN contain both short and long patterns hierarchically. In this paper, we propose a novel unsupervised time-series segmentation TCTS, which is featured with the joint optimization of two modules. The TCN-based pattern learning module aims to grasp diverse length patterns that are characterized differently by the states, while the clustering-based classification module improves the separability of the representations between the states. We conduct experiments by comparing several baselines with multiple datasets and demonstrate the superiority of TCTS.