Seasonal Time Series Data Forecasting by Using Neural Networks Multiscale Autoregressive Model

Taoufik Taoufik

doi:10.3844/ajassp.2010.1372.1378

Abstract

Problem statement: The aim of this research was to study further some latest progress of wavelet transform for time series forecasting, particularly about Neural Networks Multiscale Autoregressive (NN-MAR). Approach: There were three main issues that be considered further in this research. The first was some properties of scale and wavelet coefficients from Maximal Overlap Discrete Wavelet Transform (MODWT) decomposition, particularly at seasonal time series data. The second focused on the development of model building procedures of NN-MAR based on the properties of scale and wavelet coefficients. Then, the third was empirical study about the implementation of the proposed procedure and comparison study about the forecast accuracy of NN-MAR to other forecasting models. Results: The results showed that MODWT at seasonal time series data also has seasonal pattern for scale coefficient, whereas the wavelet coefficients are stationer. The result of model building procedure development yielded a new proposed procedure of NN-MAR model for seasonal time series forecasting. In general, this procedure accommodated input lags of scale and wavelet coefficients and other additional seasonal lags. In addition, the result showed that the proposed procedure works well for determining the best NN-MAR model for seasonal time series forecasting. Conclusion: The comparison study of forecast accuracy showed that the NN-MAR model yields better forecast than MAR and ARIMA models.

Highlights

Neural network has been proposed in many researches about different kinds of statistical analysis
The aim of this research is to develop an accurate procedure for Wavelet Neural Network (WNN) modeling of seasonal time series data and to compare the forecast accuracy with Multiscale Autoregressive (MAR) and ARIMA models
Based on the scale and wavelet coefficients pattern, the proposed of WNN model building procedure for time series data forecasting will be developed

Summary

Introduction

Neural network has been proposed in many researches about different kinds of statistical analysis. Feedforward Neural Network (FFNN) is applied in electricity demand forecasting Taylor et al (2006), General Regression Neural Network (GRNN) is used in exchange rates forecasting and Recurrent Neural Network (RNN) has been applied in detecting changes in autocorellated process for quality monitoring. Different from those previous researches, here, the predictors or the inputs are not the lags of the variables or the data variables, but they are the coefficients from wavelet transformation. Wavelet transformation gives good decomposition from a signal or time series, so that the structure can be evaluated by parametric or nonparametric models

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Conclusion