Comparison of ARIMA, neural networks and hybrid models in time series: tourist arrival forecasting

Aslanargun A., Mammadov M., Yazici B., Yolacan S.

JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, vol.77, no.1, pp.29-53, 2007 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 77 Issue: 1
  • Publication Date: 2007
  • Doi Number: 10.1080/10629360600564874
  • Journal Name: JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus
  • Page Numbers: pp.29-53
  • Keywords: time series, ARIMA, neural networks, backpropagation, radial basis function network, hybrid models, COMBINING FORECASTS, MOMENTUM, COMBINATION, ACCURACY, DESCENT, DEMAND, ISSUES, ERRORS
  • Anadolu University Affiliated: Yes

Abstract

For time series forecasting, different artificial neural network (ANN) and hybrid models are recommended as alternatives to commonly used autoregressive integrated moving average (ARIMA) models. Recently, combined models with both linear and nonlinear models have greater attention. In this article, ARIMA, linear ANN, multilayer perceptron (MLP), and radial basis function network (RBFN) models are considered along with various combinations of these models for forecasting tourist arrivals to Turkey. Comparison of forecasting performances shows that models with nonlinear components give a better performance.