Forecasting of telecommunications time-series via an Orthogonal Least Squares-based fuzzy model
Paris Mastorocostas, Constantinos S. Hilas, Stergiani C. Dova, Dimitrios Varsamis · 2012
An application of fuzzy modeling to the problem of telecommunications data prediction is proposed in this paper. The model building process is a two-stage sequential algorithm, based on the Orthogonal Least Squares (OLS) technique. Particularly, the OLS is first employed to partition the input space and determine the number of fuzzy rules and the premise parameters. In the sequel, a second orthogonal estimator determines the input terms which should be included in the consequent part of each fuzzy rule and calculate their parameters. Input selection is automatically performed, given a large input candidate set. Real world telecommunications data are used in order to highlight the characteristics of the proposed forecaster and to provide a comparative analysis with well-established forecasting models.