A TSK-based fuzzy system for telecommunications time-series forecasting

A two-stage model-building process for generating a Takagi-Sugeno-Kang fuzzy forecasting system is proposed in this paper. Particularly, the Subtractive Clustering (SC) method is first employed to partition the input space and determine the number of fuzzy rules and the premise parameters. In the se...

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Bibliographic Details
Published in2012 6th IEEE International Conference Intelligent Systems pp. 146 - 151
Main Authors Mastorocostas, P. A., Hilas, C. S., Dova, S. C., Varsamis, D. N.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2012
Subjects
Biological system modeling
Computational modeling
Forecasting
fuzzy modeling
Input variables
orthogonal least squares
Predictive models
subtractive clustering
telecommunications time-series
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Summary:A two-stage model-building process for generating a Takagi-Sugeno-Kang fuzzy forecasting system is proposed in this paper. Particularly, the Subtractive Clustering (SC) method is first employed to partition the input space and determine the number of fuzzy rules and the premise parameters. In the sequel, an Orthogonal Least Squares (OLS) estimator determines the input terms which should be included in the consequent part of each fuzzy rule and calculate their parameters. A comparative analysis with well-established forecasting models is conducted on real world tele-communications data, in order to investigate the forecasting capabilities of the proposed scheme.
ISBN:1467322768
9781467322768
ISSN:1541-1672
1941-1294
DOI:10.1109/IS.2012.6335128