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Modeling of prediction system: an application of nearest neighbor approach to chaotic data

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Main Authors: Nor Zila, Abd Hamid, Mohd Salmi, Md Noorani
Other Authors: nor_zila@yahoo.com
Format: Article
Language:English
Published: Institute of Engineering Mathematics, Universiti Malaysia Perlis 2014
Subjects:
Chaos theory
Chaotic data
Nearest neighbour approach
Zeroth-order approximation method
k-nearest neighbor approximation method
Weighted distance approximation method
Prediction
Logistic map
Online Access:http://dspace.unimap.edu.my:80/dspace/handle/123456789/35641
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spelling my.unimap-356412018-07-24T01:53:42Z Modeling of prediction system: an application of nearest neighbor approach to chaotic data Nor Zila, Abd Hamid Mohd Salmi, Md Noorani nor_zila@yahoo.com Chaos theory Chaotic data Nearest neighbour approach Zeroth-order approximation method k-nearest neighbor approximation method Weighted distance approximation method Prediction Logistic map Link to publisher's homepage at http://amci.unimap.edu.my This paper is about modeling of chaotic systems via nearest neighbor approach. This approach holds the principle that future data can be predicted using past data information. Here, all the past data known as neighbors. There are various prediction models that have been developed through this approach. In this paper, the zeroth-order approximation method (ZOAM) and improved ZOAM, namely the k-nearest neighbor approximation (KNNAM) and weighted distance approximation method (WDAM) were used. In ZOAM, only one nearest neighbor is used to predict future data while KNNAM uses more than one nearest neighbor and WDAM add the distance element for prediction process. These models were used to predict one of the chaotic data, Logistic map. 3008 Logistic map data has been produced, in which the first 3000 data were used to train the model while the rest is used to test the performance of the model. Correlation coefficient and average absolute error are used to view the performance of the model. The prediction results by the three models are in excellent agreement with the real data. This shows that the nearest neighbor approach works well to predict the chaotic data. Unfortunately, increasing the number of nearest neighbors from ZOAM to KNNAM not managed to improve prediction performance. However, the added element of the distance is a great idea for improving prediction performance. Overall, WDAM is the best model to predict the chaotic data compared to ZOAM and KNNAM. 2014-06-17T12:44:10Z 2014-06-17T12:44:10Z 2013 Article Applied Mathematics and Computational Intelligence (AMCI), vol.2 (1), 2013, pages 137-148 2289-1315 (print) 2289-1323 (online) http://dspace.unimap.edu.my:80/dspace/handle/123456789/35641 http://amci.unimap.edu.my/Vol_2_1_2013/07.html en Institute of Engineering Mathematics, Universiti Malaysia Perlis
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Chaos theory
Chaotic data
Nearest neighbour approach
Zeroth-order approximation method
k-nearest neighbor approximation method
Weighted distance approximation method
Prediction
Logistic map
spellingShingle Chaos theory
Chaotic data
Nearest neighbour approach
Zeroth-order approximation method
k-nearest neighbor approximation method
Weighted distance approximation method
Prediction
Logistic map
Nor Zila, Abd Hamid
Mohd Salmi, Md Noorani
Modeling of prediction system: an application of nearest neighbor approach to chaotic data
description Link to publisher's homepage at http://amci.unimap.edu.my
author2 nor_zila@yahoo.com
author_facet nor_zila@yahoo.com
Nor Zila, Abd Hamid
Mohd Salmi, Md Noorani
format Article
author Nor Zila, Abd Hamid
Mohd Salmi, Md Noorani
author_sort Nor Zila, Abd Hamid
title Modeling of prediction system: an application of nearest neighbor approach to chaotic data
title_short Modeling of prediction system: an application of nearest neighbor approach to chaotic data
title_full Modeling of prediction system: an application of nearest neighbor approach to chaotic data
title_fullStr Modeling of prediction system: an application of nearest neighbor approach to chaotic data
title_full_unstemmed Modeling of prediction system: an application of nearest neighbor approach to chaotic data
title_sort modeling of prediction system: an application of nearest neighbor approach to chaotic data
publisher Institute of Engineering Mathematics, Universiti Malaysia Perlis
publishDate 2014
url http://dspace.unimap.edu.my:80/dspace/handle/123456789/35641
_version_ 1643804040486715392
score 13.214268

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