Title :
Traffic Network Models Correlated
Author :
Hernandez, Cesar A S ; Pedraza, Luis F M
Author_Institution :
Technol. Into Electr., Distrital Francisco Jose de Caldas Univ., Bogota, Colombia
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.
Keywords :
WiMax; autoregressive moving average processes; fuzzy reasoning; time series; traffic engineering computing; ANFIS model; ARIMA model; Wimax traffic data series; adaptive network-based fuzzy inference system; artificial intelligence technique; autoregressive integrated moving average; mean square error sum criteria; time series forecasting; traffic network models; Adaptation model; Correlation; Data models; Mathematical model; Predictive models; Software; Time series analysis;
Conference_Titel :
Computational Intelligence and Software Engineering (CiSE), 2010 International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-5391-7
Electronic_ISBN :
978-1-4244-5392-4
DOI :
10.1109/CISE.2010.5676921
