Economic Time Series Forecasting: Box-Jenkins Methodology, Signal Processing and Neural Network Approach

Dusan Marcek,Michaela Acova

doi:10.26552/com.c.2003.4.55-58

Abstract

This paper is devoted to the presentation of methods of economic time series analysis and modelling using the Box-Jenkins methodology, the signal processing approach and the feedforward neural network technique. Some results of our research on time series modelling with emphasis on potential improving forecast accuracy are presented here. The assessment of the particular models has been made using the root mean square error.

Highlights

Artificial neural network (ANN) is being applied to many problems far removed from their first beginnings
There is much controversy about the application of traditional statistical or econometric models and the ANN approaches within the field of economic time series modelling and forecasting. These controversies are based on the assumptions that there is no consensus at all on whether there is chaos in economic time series or not
Many of modelling techniques of autoregressive processes are based on recent developments in time series analysis recently consolidated and presented by Box and Jenkins [4]

Summary

Introduction

Artificial neural network (ANN) is being applied to many problems far removed from their first beginnings. Application to management problems have included predicting bankruptcy [3], predicting ratings of corporate bonds [11], forecasting financial markets [6] and time series forecasting [7]. Their main strengths lie in pattern recognition and have been a hot topic of research for many years now. There is much controversy about the application of traditional statistical or econometric models and the ANN approaches within the field of economic time series modelling and forecasting. Many of modelling techniques of autoregressive processes are based on recent developments in time series analysis recently consolidated and presented by Box and Jenkins [4]. By using only the actual and forecast values within the ex post forecasting period only, the accuracy of the model can be calculated

Application of the Box-Jenkins methodology in the stock prediction problem

Stock price prediction using adaptive signal processing procedures

Neural network approach

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