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A Comparative Study on FFNN and ARIMA Model in the Presence of Outliers

by K. Senthamarai Kannan, V. Deneshkumar, S. Arumugam
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 76 - Number 17
Year of Publication: 2013
Authors: K. Senthamarai Kannan, V. Deneshkumar, S. Arumugam
10.5120/13338-0621

K. Senthamarai Kannan, V. Deneshkumar, S. Arumugam . A Comparative Study on FFNN and ARIMA Model in the Presence of Outliers. International Journal of Computer Applications. 76, 17 ( August 2013), 12-18. DOI=10.5120/13338-0621

@article{ 10.5120/13338-0621,
author = { K. Senthamarai Kannan, V. Deneshkumar, S. Arumugam },
title = { A Comparative Study on FFNN and ARIMA Model in the Presence of Outliers },
journal = { International Journal of Computer Applications },
issue_date = { August 2013 },
volume = { 76 },
number = { 17 },
month = { August },
year = { 2013 },
issn = { 0975-8887 },
pages = { 12-18 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume76/number17/13338-0621/ },
doi = { 10.5120/13338-0621 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:48:39.611095+05:30
%A K. Senthamarai Kannan
%A V. Deneshkumar
%A S. Arumugam
%T A Comparative Study on FFNN and ARIMA Model in the Presence of Outliers
%J International Journal of Computer Applications
%@ 0975-8887
%V 76
%N 17
%P 12-18
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Time series data mining (TSDM) techniques explores large amount of time series data in search of interesting relationships among variables. The TSDM methods overcome limitations including stationarity and linearity requirements of traditional time series analysis by adapting data mining concepts for analyzing time series data. The Feed Forward Neural Net is one of the most widely used neural nets. In this paper, the Feed Forward Neural Nets architecture is examined and compared with Statistical Time Series Auto regressive integrated moving average (ARIMA) model for prediction of agricultural production. The performance by ANN model and Time series model for prediction are examined using visualization technique and statistical test and the results are illustrated numerically and graphically

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Index Terms

Computer Science
Information Sciences

Keywords

Feed Forward Neural Nets ANN ARIMA Outliers Forecasting and MSE.

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