Time Series Segmentation Using an Adaptive Resource Allocating Vector Quantization Network Based on Change Detection

We present a novel architecture for unsupervised time series segmentation, which is based on change detection rather than traditional error minimization. The architecture, which consists of a simple vector quantizer which dynamically allocates model vectors when needed, is able to split a multi-dimensional noisy time series generated from the sensors of a mobile robot into relevant segments using just a single presentation of the data. We compare the architecture with an existing system created by Nolfi and Tani (1999), which is based on traditional overall error minimization, and note that our system is able to detect stable and distinct signal regions, which are not detected by their system.