PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Embedding-based Subsequence Matching in Time Series Databases
Panagiotis Papapetrou, Vassilis Athitsos, Michalis Potamias, George Kollios and Dimitrios Gunopulos
ACM Transactions on Database Systems Volume 36, Number 3, 2011.

Abstract

We propose an embedding-based framework for subsequence matching in time-series databases that im- proves the efficiency of processing subsequence matching queries under the Dynamic Time Warping (DTW) distance measure. This framework partially reduces subsequence matching to vector matching, using an embedding that maps each query sequence to a vector and each database time series into a sequence of vec- tors. The database embedding is computed offline, as a preprocessing step. At runtime, given a query object, an embedding of that object is computed online. Relatively few areas of interest are efficiently identified in the database sequences by comparing the embedding of the query with the database vectors. Those areas of interest are then fully explored using the exact DTW-based subsequence matching algorithm. We apply the proposed framework to define two specific methods. The first method focuses on time-series subsequence matching under unconstrained Dynamic Time Warping. The second method targets subsequence matching under constrained Dynamic Time Warping (cDTW), where warping paths are not allowed to stray too much off the diagonal. In our experiments, good trade-offs between retrieval accuracy and retrieval efficiency are obtained for both methods, and the results are competitive with respect to current state-of-the-art methods.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:8920
Deposited By:Panagiotis Papapetrou
Deposited On:21 February 2012