Multidimensional non-stationary time-series segmentation based on fuzzy clusterization
DOI:
https://doi.org/10.15587/1729-4061.2012.4903Keywords:
time series, data prediction, segmentation.Abstract
The problem of the large time series prediction is considered. The method of construction of the brief self-weighted time series, based on segmentation of initial row and analysis of the got segments is offered. Expansion of the known methods of prediction on the self-weighted time series is executed.References
- Keogh, E. An online algorithm for segmenting time series [Текст] / E. Keogh, S. Chu, D. Hart, M. Pazzani // IEEE International Conference on Data Mining. – 2001.
- Keogh, E. An enhanced representation of time series which allows fast and accurate classification, clustering and relevance feedback [Текст] / E. Keogh, M. Pazzani // 4th Int. Conf. on KDD. – 1998. – P. 239–243.
- Abonyi, J. Modified Gath-Geva fuzzy clustering for identification of Takagi-Sugeno fuzzy models [Текст] / J. Abonyi, F. Szeifert, R. Babuska // IEEE Systems, Man and Cybernetics, Part B. – 2002. – P. 612–621.
- Vasko, K. Estimating the number of segments in time series data using permutation tests [Текст] / K. Vasko, H.T.T. Toivonen // IEEE International Conference on Data Mining. – 2002. – P. 466–473.
- Kaymak, U. Compatible cluster merging for fuzzy modelling / U. Kaymak, R. Babuska [Текст] // In Proceedings of the IEEE International Conference on Fuzzy Systems. – 1995. – P. 897–904.
- Geva, A.B. Hierarchical-fuzzy clustering of temporal-patterns and its application for time-series prediction [Текст] / A.B. Geva // Pattern Recognition Letters. – 1999. – P. 1519-1532.
- Gath, I. Unsupervised optimal fuzzy clusteringс [Текст] / I. Gath, A.B. Geva // IEEE Transactions on Pattern Analysis and Machine Intelligence. – 1989. – P. 773–781.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2014 Алексей Константинович Тищенко, Ирина Павловна Плисс
This work is licensed under a Creative Commons Attribution 4.0 International License.
The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.
A license agreement is a document in which the author warrants that he/she owns all copyright for the work (manuscript, article, etc.).
The authors, signing the License Agreement with TECHNOLOGY CENTER PC, have all rights to the further use of their work, provided that they link to our edition in which the work was published.
According to the terms of the License Agreement, the Publisher TECHNOLOGY CENTER PC does not take away your copyrights and receives permission from the authors to use and dissemination of the publication through the world's scientific resources (own electronic resources, scientometric databases, repositories, libraries, etc.).
In the absence of a signed License Agreement or in the absence of this agreement of identifiers allowing to identify the identity of the author, the editors have no right to work with the manuscript.
It is important to remember that there is another type of agreement between authors and publishers – when copyright is transferred from the authors to the publisher. In this case, the authors lose ownership of their work and may not use it in any way.
