Time series forecasting with neural networks: A comparative study using the airline data

Julian Faraway, Chris Chatfield

Research output: Contribution to journalArticlepeer-review

307 Citations (SciVal)


This case-study fits a variety of neural network (NN) models to the well-known airline data and compares the resulting forecasts with those obtained from the Box-Jenkins and Holt-Winters methods. Many potential problems in fitting NN models were revealed such as the possibility that the fitting routine may not converge or may converge to a local minimum. Moreover it was found that an NN model which fits well may give poor out-of-sample forecasts. Thus we think it is unwise to apply NN models blindly in 'black box' mode as has sometimes been suggested. Rather, the wise analyst needs to use traditional modelling skills to select a good NN model, e.g. to select appropriate lagged variables as the 'inputs'. The Bayesian information criterion is preferred to Akaike's information criterion for comparing different models. Methods of examining the response surface implied by an NN model are examined and compared with the results of alternative nonparametric procedures using generalized additive models and projection pursuit regression. The latter imposes less structure on the model and is arguably easier to understand.

Original languageEnglish
Pages (from-to)231-250
Number of pages20
JournalJournal of the Royal Statistical Society. Series C: Applied Statistics
Issue number2
Publication statusPublished - 1 Dec 1998


  • Airline model
  • Akaike information criterion
  • Autoregressive integrated moving average model
  • Bayesian information criterion
  • Box-Jenkins forecasting
  • Generalized additive model
  • Holt-Winters forecasting
  • Projection pursuit regression

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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