A New Temporal Pattern Identification Method For Characterization And Prediction Of Complex Time Series Events

Authors

Richard J. Povinelli, Marquette UniversityFollow
Xin Feng, Marquette UniversityFollow

Document Type

Article

Language

eng

Format of Original

14 p.

Publication Date

3-2003

Publisher

Institute of Electrical and Electronics Engineers

Source Publication

IEEE Transactions on Knowledge and Data Engineering

Source ISSN

1041-4347

Original Item ID

doi: 10.1109/TKDE.2003.1185838

Abstract

A new method for analyzing time series data is introduced in this paper. Inspired by data mining, the new method employs time-delayed embedding and identifies temporal patterns in the resulting phase spaces. An optimization method is applied to search the phase spaces for optimal heterogeneous temporal pattern clusters that reveal hidden temporal patterns, which are characteristic and predictive of time series events. The fundamental concepts and framework of the method are explained in detail. The method is then applied to the characterization and prediction, with a high degree of accuracy, of the release of metal droplets from a welder. The results of the method are compared to those from a Time Delay Neural Network and the C4.5 decision tree algorithm.

Comments

Accepted version. IEEE Transactions on Knowledge and Data Engineering, Vol. 15, No. 2 (March/April 2003): 339-352. DOI. © 2003 The Institute of Electrical and Electronics Engineers. Used with permission.

Recommended Citation

Povinelli, Richard J. and Feng, Xin, "A New Temporal Pattern Identification Method For Characterization And Prediction Of Complex Time Series Events" (2003). Electrical and Computer Engineering Faculty Research and Publications. 102.
https://epublications.marquette.edu/electric_fac/102