The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index.
Time Series Forecasting with Neural Networks: A Comparative Study Using the Air Line Data
Open accessAbstract
SUMMARY: This case-study fits a variety of neural network (NN) models to the well-known air line 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.
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Cites
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Cited by
Maier, H. R., & Dandy, G. C. (2000) · Environmental Modelling & Software · doi:10.1016/s1364-8152(99)00007-9 · Cited by 2,377
Neural network forecasting for seasonal and trend time series
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Bandara, K., Bergmeir, C., & Smyl, S. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.112896 · Cited by 435
Electric load forecasting in smart grids using Long-Short-Term-Memory based Recurrent Neural Network
Zheng, J., Xu, C., Zhang, Z., & Li, X. (2017) · 2017 51st Annual Conference on Information Sciences and Systems (CISS) · doi:10.1109/ciss.2017.7926112 · Cited by 341
Metadata
- Authors
- Faraway, J., & Chatfield, C.
- Venue
- Journal of the Royal Statistical Society Series C: Applied Statistics
- Publisher
- Oxford University Press (OUP)
- Published
- 1998-06-01
- Volume
- 47
- Issue
- 2
- Pages
- 231-250
- ISSN
- 0035-9254 (ISSN-L)
- 1467-9876
- OpenAlex
- W2145856394
- Cited by
- 419
- References
- 47
- FWCI
- 10.16
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Faraway, J., & Chatfield, C. (1998). Time Series Forecasting with Neural Networks: A Comparative Study Using the Air Line Data. Journal of the Royal Statistical Society Series C: Applied Statistics. https://doi.org/10.1111/1467-9876.00109
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
