short-paper

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

Authors:

Francisco Puente,

Cesar A. Hernandez S.,

Octavio J. Salcedo P.Authors Info & Claims

MoMM '09: Proceedings of the 7th International Conference on Advances in Mobile Computing and Multimedia

Pages 277 - 281

https://doi.org/10.1145/1821748.1821801

Published: 14 December 2009 Publication History

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.

References

[1]

Alzate, Marco Aurelio. Modelos de tráfico en análisis y control de redes de comunicaciones. En: Revista de ingeniería de la Universidad Distrital Francisco José de Caldas. Bogotá. Vol. 9, No. 1 (Junio 2004); p. 63--87.

[2]

Box, G. E. P. y Jenkins, Gwilym M. Time series analysis: Forecasting and control. Revised Edition. Oakland, California: Editorial Holden-Day, 1976.

Digital Library

[3]

Brockwell, P. J. On continuous-time ARMA processes. En: Handbook of statistics. Elsevier, Amsterdam: Vol. 19, 2001. p. 249--276.

[4]

Camerano Fuentes, Rafael. Teoría de colas. Bogota: Fondo de publicaciones Universidad Distrital Francisco José de Caldas, 1997.

[5]

Correa Moreno, Emilia. Series de tiempo: conceptos básicos. Medellín: Universidad Nacional de Colombia, Facultad de Ciencias, Departamento de matemáticas, 2004.

[6]

Couch, L. Digital and analog communication system. New Jersey: Prentice Hall, 2001.

Digital Library

[7]

Davis, R. A. Maximum likelihood estimation for MA(1) processes with a root on or near the unit circle. In: Econometric theory. Vol. 12, 1996. p. 1--29.

[8]

Dethe, Chandrashekhar y WAKDE D. G. On the prediction of packet process in network traffic using FARIMA time series model. Department of Electronics, College of Engineering, India. 2003.

[9]

Guerrero Guzman, Víctor Manuel. Análisis estadístico de series de tiempo económicas. Segunda edición. México: Editorial Thomson, 2003.

[10]

Halang, Z. Li and Chen, G. Integration of fuzzy logic and chaos theory. Springer, 2006.

Digital Library

[11]

Jang, J.-S. ANFIS: Adaptive-network-based fuzzy inference systems. En: IEEE Transactions on systems, man, and cybernetics. Vol. 23, 1993.

[12]

Jang, J.-S. and Mizutani, Sun E. Neuro-fuzzy and soft computing---A computational approach to learning and machine intelligence. Prentice Hall, 1997.

Digital Library

[13]

Makridakis, Spyros G.; Wheelwright, Steven C. y Hyndman, Rob J. Forecasting: methods and applications. Tercera edición. USA: Editorial Wiley, 1997.

[14]

Pajouh, Danech. Methodology for traffic forescating. The French National Institute for Transport and Safety Research (INRETS). Arcuel. 2002.

[15]

Stallings, William. Comunicaciones y redes de computadores. Séptima edición. Madrid: Prentice Hall, 2004.

[16]

Zak, S. Systems and control. Oxford: oxford university Press, 2003.

Cited By

Mukherjee TGupta SMitra A(2023)Hyper Parameterized LSTM Models for Predicting NSE Intraday Bias Based on Global Market TrendsIntelligent Human Centered Computing10.1007/978-981-99-3478-2_13(138-146)Online publication date: 15-Jun-2023
https://doi.org/10.1007/978-981-99-3478-2_13

Recommendations

Implementation of stacking based ARIMA model for prediction of Covid-19 cases in India
Graphical abstract
Fig. 2: Pictorial description of the stack based ensemble ARIMA model.

Display Omitted
Highlights
- Development of Hybrid time-series model primarily for short term forecasts of COVID-19 pandemic in India.
Abstract Background
Time-series forecasting has a critical role during pandemics as it provides essential information that can lead to abstaining from the spread of the disease. The novel coronavirus disease, COVID-19, is spreading ...
Forecasting with information extracted from the residuals of ARIMA in financial time series using continuous wavelet transform

Time series of financial or economic data are often considered to have certain trends and patterns. It is believed that the study of historical patterns helps in the forecasting into the future. ARIMA model is one of the popular models for the task. ...
Self-projecting time series forecast - an online stock trend forecast system
ISPA'03: Proceedings of the 2003 international conference on Parallel and distributed processing and applications

This paper explores the applicability of time series analysis for stock trend forecast and presents the Self-projecting Time Series Forecasting (STSF) System we have developed. The basic idea behind this system is online discovery of mathematical ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

MoMM '09: Proceedings of the 7th International Conference on Advances in Mobile Computing and Multimedia

December 2009

663 pages

ISBN:9781605586595

DOI:10.1145/1821748

General Chair:
Gabriele Kotsis
Johannes Kepler University Linz, Austria
,
Program Chairs:
David Taniar
Monash University, Australia
,
Eric Pardede
La Trobe University, Australia

Copyright © 2009 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Johannes Kepler University

In-Cooperation

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 December 2009

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper

Conference

MoMM '09

Sponsor:

MoMM '09: 7th International Conference on Mobile Computing and Multimedia

December 14 - 16, 2009

Kuala Lumpur, Malaysia

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
223
Total Downloads

Downloads (Last 12 months)2
Downloads (Last 6 weeks)0

Reflects downloads up to 05 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Mukherjee TGupta SMitra A(2023)Hyper Parameterized LSTM Models for Predicting NSE Intraday Bias Based on Global Market TrendsIntelligent Human Centered Computing10.1007/978-981-99-3478-2_13(138-146)Online publication date: 15-Jun-2023
https://doi.org/10.1007/978-981-99-3478-2_13

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents