On the Stationarity of Multivariate Time Series for Correlation-Based Data Analysis

Multivariate time series (MTS) data sets are common in various multimedia, medical and financial application domains. These applications perform several data-analysis operations on large number of MTS data sets such as similarity searches, feature-subset-selection, clustering and classifications. Correlation-based techniques, such as Principal Component Analysis (PCA), have proven to improve the efficiency of many of the above-mentioned data-analysis operations on MTS, which implies that the correlation coefficients concisely represent the original MTS data. However, if the statistical properties (e.g., variance) of MTS data change over time dimension, i.e., MTS data is nonstationary, the correlation coefficients are not stable. In this paper, we propose to utilize the stationarity of the MTS data sets, in order to represent the original MTS data more stably, as well as concisely with the correlation coefficients. That is, before performing any correlation-based data analysis, w...

• Type: Conference
• Year: 2005
• Where: ICDM
• Authors: Kiyoung Yang, Cyrus Shahabi
• Keywords: Data Mining, Data Sets, ICDM 2005, MTS Data, Mts Data Set