Comparative analysis of time series techniques ARIMA and ANFIS to forecast Wimax traffic

Authors:
Francisco Puente;Cesar A. Hernandez S.;Octavio J. Salcedo P.
Affiliations:
Distrital University, Bogota, Colombia;Distrital University, Bogota, Colombia;Distrital University, Bogota, Colombia
Venue:
Proceedings of the 7th International Conference on Advances in Mobile Computing and Multimedia
Year:
2009

Citing 4
Cited 0

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Digital and Analog Communication Systems

Digital and Analog Communication Systems
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Time Series Analysis, Forecasting and Control
Integration of Fuzzy Logic and Chaos Theory

Integration of Fuzzy Logic and Chaos Theory

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Abstract

The procedure and main result of a comparative study based on using an autoregressive model and an artificial intelligence technique applied to a Wimax traffic data series forecasting task are presented in this document. The time series forecasting methods being compared are: ANFIS model (Adaptive Network-based Fuzzy Inference Sys-tem) and ARIMA model (Auto-Regressive Integrated Moving Average). This article aims to present significant data showing each technique performance under the criteria of mean square error sum and the required processing time. As a result, in this study ARIMA models developed under RATS platforms are compared to the ANFIS models developed through MATLAB.