Time Series Prediction via Two-Step Clustering
Abstract
Linear and nonlinear models for time series analysis and prediction are well-established. Clustering methods have also been applied to this area. This paper explores a framework that can be used to cluster time series data. The range of values of a time series is clustered. Then the time series is clustered by data windows that flow into the initial set of value clusters. This allows predictive temporal patterns to be discovered across the whole range of values.
Recommended Citation
C. Smith and D. C. Wunsch, "Time Series Prediction via Two-Step Clustering," Proceedings of the International Joint Conference on Neural Networks, Institute of Electrical and Electronics Engineers (IEEE), Jan 2015.
The definitive version is available at https://doi.org/10.1109/IJCNN.2015.7280586
Meeting Name
International Joint Conference on Neural Networks, IJCNN 2015 (2015: Jul. 12-17, Killarney, Ireland)
Department(s)
Electrical and Computer Engineering
Research Center/Lab(s)
Center for High Performance Computing Research
International Standard Book Number (ISBN)
978-1479919604
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2015 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Jan 2015
