THE EFFECT OF DECOMPOSITION METHOD AS DATA PREPROCESSING ON NEURAL NETWORKS MODEL FOR FORECASTING TREND AND SEASONAL TIME SERIES

Recently, one of the central topics for the neural networks (NN) community is the issue of data preprocessing on the use of NN. In this paper, we will investigate this topic particularly on the effect of Decomposition method as data processing and the use of NN for modeling effectively time series w...

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Main Authors:	Subanar Subanar, Suhartono Suhartono
Format:	Article
Language:	English
Published:	Petra Christian University 2006-01-01
Series:	Jurnal Teknik Industri
Subjects:	decomposition data preprocessing neural networks trend seasonality time series forecasting.
Online Access:	http://puslit2.petra.ac.id/ejournal/index.php/ind/article/view/16556

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spelling	doaj-0b3914dce0314596bcfc506598ad1aaa2020-11-25T01:19:19ZengPetra Christian UniversityJurnal Teknik Industri1411-24852006-01-0182156164THE EFFECT OF DECOMPOSITION METHOD AS DATA PREPROCESSING ON NEURAL NETWORKS MODEL FOR FORECASTING TREND AND SEASONAL TIME SERIESSubanar SubanarSuhartono SuhartonoRecently, one of the central topics for the neural networks (NN) community is the issue of data preprocessing on the use of NN. In this paper, we will investigate this topic particularly on the effect of Decomposition method as data processing and the use of NN for modeling effectively time series with both trend and seasonal patterns. Limited empirical studies on seasonal time series forecasting with neural networks show that some find neural networks are able to model seasonality directly and prior deseasonalization is not necessary, and others conclude just the opposite. In this research, we study particularly on the effectiveness of data preprocessing, including detrending and deseasonalization by applying Decomposition method on NN modeling and forecasting performance. We use two kinds of data, simulation and real data. Simulation data are examined on multiplicative of trend and seasonality patterns. The results are compared to those obtained from the classical time series model. Our result shows that a combination of detrending and deseasonalization by applying Decomposition method is the effective data preprocessing on the use of NN for forecasting trend and seasonal time series. http://puslit2.petra.ac.id/ejournal/index.php/ind/article/view/16556decompositiondata preprocessingneural networkstrendseasonalitytime seriesforecasting.
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Subanar Subanar Suhartono Suhartono
spellingShingle	Subanar Subanar Suhartono Suhartono THE EFFECT OF DECOMPOSITION METHOD AS DATA PREPROCESSING ON NEURAL NETWORKS MODEL FOR FORECASTING TREND AND SEASONAL TIME SERIES Jurnal Teknik Industri decomposition data preprocessing neural networks trend seasonality time series forecasting.
author_facet	Subanar Subanar Suhartono Suhartono
author_sort	Subanar Subanar
title	THE EFFECT OF DECOMPOSITION METHOD AS DATA PREPROCESSING ON NEURAL NETWORKS MODEL FOR FORECASTING TREND AND SEASONAL TIME SERIES
title_short	THE EFFECT OF DECOMPOSITION METHOD AS DATA PREPROCESSING ON NEURAL NETWORKS MODEL FOR FORECASTING TREND AND SEASONAL TIME SERIES
title_full	THE EFFECT OF DECOMPOSITION METHOD AS DATA PREPROCESSING ON NEURAL NETWORKS MODEL FOR FORECASTING TREND AND SEASONAL TIME SERIES
title_fullStr	THE EFFECT OF DECOMPOSITION METHOD AS DATA PREPROCESSING ON NEURAL NETWORKS MODEL FOR FORECASTING TREND AND SEASONAL TIME SERIES
title_full_unstemmed	THE EFFECT OF DECOMPOSITION METHOD AS DATA PREPROCESSING ON NEURAL NETWORKS MODEL FOR FORECASTING TREND AND SEASONAL TIME SERIES
title_sort	effect of decomposition method as data preprocessing on neural networks model for forecasting trend and seasonal time series
publisher	Petra Christian University
series	Jurnal Teknik Industri
issn	1411-2485
publishDate	2006-01-01
description	Recently, one of the central topics for the neural networks (NN) community is the issue of data preprocessing on the use of NN. In this paper, we will investigate this topic particularly on the effect of Decomposition method as data processing and the use of NN for modeling effectively time series with both trend and seasonal patterns. Limited empirical studies on seasonal time series forecasting with neural networks show that some find neural networks are able to model seasonality directly and prior deseasonalization is not necessary, and others conclude just the opposite. In this research, we study particularly on the effectiveness of data preprocessing, including detrending and deseasonalization by applying Decomposition method on NN modeling and forecasting performance. We use two kinds of data, simulation and real data. Simulation data are examined on multiplicative of trend and seasonality patterns. The results are compared to those obtained from the classical time series model. Our result shows that a combination of detrending and deseasonalization by applying Decomposition method is the effective data preprocessing on the use of NN for forecasting trend and seasonal time series.
topic	decomposition data preprocessing neural networks trend seasonality time series forecasting.
url	http://puslit2.petra.ac.id/ejournal/index.php/ind/article/view/16556
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THE EFFECT OF DECOMPOSITION METHOD AS DATA PREPROCESSING ON NEURAL NETWORKS MODEL FOR FORECASTING TREND AND SEASONAL TIME SERIES

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