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A New Temporal Pattern Identification Method for Characterization and Prediction of Complex Time Series Events
March/April 2003 (vol. 15 no. 2)
pp. 339-352

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 fundemantal 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.

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Index Terms:
Temporal pattern identification, time series analysis, data mining, time delay embedding, optimization clustering, and genetic algorithms.
Richard J. Povinelli, Xin Feng, "A New Temporal Pattern Identification Method for Characterization and Prediction of Complex Time Series Events," IEEE Transactions on Knowledge and Data Engineering, vol. 15, no. 2, pp. 339-352, March-April 2003, doi:10.1109/TKDE.2003.1185838
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