RIPTIDE: Segmenting data using multiple resolutions
Tom Armstrong, Tim Oates · 2007
Segmenting real-valued data, be it speech waveforms into words and phrases or temperature readings into environmental epochs, is a challenging, open problem. We introduce an unsupervised, domain-independent algorithm, RIPTIDE, that discovers segments in real-valued time series data while constructing a hierarchy of segments. Our top-down approach begins with a coarse approximation of the input data, finds segment boundaries, and recursively considers discovered segments with a finer resolution. We demonstrate the drawbacks of an existing segmentation algorithm and the multiresolution capabilities of a discretization method for time series.