An efficient k nearest neighbor search for multivariate time series

Abstract

Multivariate time series (MTS) datasets are common in various multimedia, medical and financial applications. In order to efficiently perform k nearest neighbor searches for MTS datasets, we present a similarity measure, Eros (extended Frobenius norm), an index structure, Muse (multilevel distance-based index structure for Eros ), and a feature subset selection technique, Ropes (recursive feature elimination on common principal components for Eros). Eros is based on principal component analysis, and computes the similarity between two MTS items by measuring how close the corresponding principal components are using the eigenvalues as weights . Muse constructs each level as a distance-based index structure without using the weights, up to z levels, which are combined at the query time with the weights. Ropes utilizes both the common principal components and the weights recursively in order to select a subset of features for Eros. The experimental results show the superiority of our techniques as compared to earlier approaches.