THE EFFECT OF DECOMPOSITION METHOD AS DATA PREPROCESSING ON NEURAL NETWORKS MODEL FOR FORECASTING TREND AND SEASONAL TIME SERIES
ABSTRACT: 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.
Keywords: decomposition, data
preprocessing, neural networks, trend, seasonality, time series, forecasting
Author: Suhartono, Subanar
Journal Code: jptindustrigg060003
