An Efficient Similarity Search For Financial 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. 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⁺-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.