Abstract
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. In order to efficiently perform similarity search for financial MTS datasets, we present a distance-based index structure (Dbis) for range search and k nearest neighbor (kNN) search. The financial MTS database is parted by cluster, A MTS item is selected for each partition as reference point. The MTS items in each partition are transformed into a single dimensional space based on their similarity with respect to a reference MTS item. This allows the MTS items to be indexed by using a B <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> -tree structure. An extended Frobenius norm (Eros) is used to compare the similarity between MTS items. Several experiments on a financial MTS database are performed and the results show the effectiveness of Dbis.
Published Version
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