Feature Selection and Classification Techniques for Multivariate Time Series

Multivariate time series (MTS) data sets are common in many multimedia, medical, process industry and financial applications such as gesture recognition, video sequence matching, EEG/ECG data analysis or prediction of abnormal situation or trend of stock price. MTS data sets are high dimensional as they consist of a series of observations of many variables (multidimendsional variable) at a time. For analysis of MTS data in order to extract knowledge, a compact representation is needed. For feature subset selection for MTS data sets, popular techniques for machine learning or pattern recognition problems are modified. This paper summarizes the current techniques for feature subset selection and classification for MTS data sets.