Abstract:A recurrent neural network-based forecasting system for telecommunications call volume is proposed in this work. In particular, the forecaster is a Block-Diagonal Recurrent Neural Network with internal feedback. Model's performance is evaluated by use of real-world telecommunications data, where an extensive comparative analysis with a series of existing forecasters is conducted, including both traditional models as well as neural and fuzzy approaches.

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 wellestablished forecasting models.

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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.

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