title: Similarity-Based Searching in Multi-Parameter Time Series Databases.
abstract: We present a similarity-based searching and pattern matching algorithm that identifies time series data with similar temporal dynamics in large-scale, multi-parameter databases. We represent time series segments by feature vectors that reflect the dynamical patterns of single and multi-dimensional physiological time series. Features include regression slopes at varying time scales, maximum transient changes, auto-correlation coefficients of individual signals, and cross correlations among multiple signals. We model the dynamical patterns with a Gaussian mixture model (GMM) learned with the Expectation Maximization algorithm, and compute similarity between segments as Mahalanobis distances. We evaluate the use of our algorithm in three applications: search-by-example based data retrieval, event classification, and forecasting, using synthetic and real physiologic time series from a variety of sources.
authors.affiliation: Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA, USA.
authors.forename: Lh
authors.initials: L
authors.lastname: Lehman
authors.forename: M
authors.initials: M
authors.lastname: Saeed
authors.forename: Gb
authors.initials: G
authors.lastname: Moody
authors.forename: Rg
authors.initials: R
authors.lastname: Mark