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

fYear

2010

fDate

10-12 Dec. 2010

Firstpage

1

Lastpage

6

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/CISE.2010.5676921

Filename

5676921