JOS

Abstract
Journal of Official Statistics, Vol.17, No.4, 2001. pp. 447–456

Contents
Current Issue
Personal Reference Library (PRL)
Personal Page
Archive
Search
Home


A Neural Network Model for Predicting Time Series with Interventions and a Comparative Analysis

Abstract:
A procedure for designing a multilayer perceptron for predicting time series with interventions is proposed. It is based on the generation, according to a rule emerging from an ARIMA model with interventions previously fitted, of a set of nonlinear forecasting models with interventions. Each model is approximated through a three-layered perceptron, selecting the one minimizing the Bayesian Information Criterion. The training of the multilayer perceptron is performed by three alternative learning rules, incorporating multiple repetitions, and the hidden layer size is computed by means of a grid search. A comparative analysis using time series from the Active Population Survey in Andalusia, Spain, shows a better performance of these neural network models over ARIMA models with interventions.

Keywords:
Backpropagation; backpropagation with momentum; Levenberg-Marquardt; mean squared error; mean absolute deviation; MATLAB; SAS; active population survey.

Copyright © Statistics Sweden 1996-2018.  Open Access
ISSN 0282-423X
Created and Maintained by OKS Group