Description

Time-series classification (TSC) problems involve training a classifier on a set of cases, where each case contains an ordered set of real valued attributes and a class label. Time-series classification problems arise in a wide range of fields including, but not limited to, data mining, statistics, machine learning, signal processing, environmental sciences, computational biology, image processing and chemo metrics. The MLS group have a strong track record in TSC and have published these papers in this area over the last seven years. We have contributed data sets and results to the UCR time series repository and are continuing to develop new algorithms for a wide range of TSC problems. Our core thesis regarding time series classification is that the best approach is to separate the data transformation from the classification stage. In time series analysis, dynamic time warping (DTW) is an algorithm for measuring similarity between two temporal sequences which may vary in time or speed. For instance, similarities in walking patterns could be detected using DTW, even if one person was walking faster than the other, or if there were accelerations and decelerations during the course of an observation. DTW has been applied to temporal sequences of video, audio, and graphics data indeed, any data which can be turned into a linear sequence can be analyzed with DTW. A well known application has been automatic speech recognition, to cope with different speaking speeds. Other applications include speaker recognition and online signature recognition. Also it is seen that it can be used in partial shape matching application.