The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
A dynamic artificial neural network model for forecasting time series events
Open accessAbstract
Abstract Neural networks have shown to be an effective method for forecasting time series events. Traditional research in this area uses a network with a sequential iterative learning process based on the feed-forward, back-propagation approach. In this paper we present a dynamic neural network model for forecasting time series events that uses a different architecture than traditional models. To assess the effectiveness of this method, we forecasted a number of standard benchmarks in time series research from forecasting literature. Results show that this approach is more accurate and performs significantly better than the traditional neural network and autoregressive integrated moving average (ARIMA) models.
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Cites
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892
Multilayer feedforward networks are universal approximators
Hornik, K., Stinchcombe, M., & White, H. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90020-8 · Cited by 21,728
Engle, R. F. (1982) · Econometrica · doi:10.2307/1912773 · Cited by 20,795
Cited by
Using artificial neural network models in stock market index prediction
Guresen, E., Kayakutlu, G., & Daim, T. U. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.02.068 · Cited by 786
A Bayesian regularized artificial neural network for stock market forecasting
Ticknor, J. L. (2013) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.04.013 · Cited by 517
Ghiassi, M., Skinner, J., & Zimbra, D. (2013) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.05.057 · Cited by 485
Self-citationBayesian regularisation neural network based on artificial intelligence optimisation
Yan, D., Zhou, Q., Wang, J., & Zhang, N. (2017) · International Journal of Production Research · doi:10.1080/00207543.2016.1237785 · Cited by 62
Metadata
- Authors
- Ghiassi, M., Saidane, H., & Zimbra, D. K.
- Venue
- International Journal of Forecasting
- Publisher
- Elsevier BV
- Published
- 2005-04
- Volume
- 21
- Issue
- 2
- Pages
- 341-362
- ISSN
- 0169-2070 (ISSN-L)
- OpenAlex
- W1996605950
- Cited by
- 257
- References
- 3
- FWCI
- 7.86
- Type
- journal article
- Language
- en
- Open access
- green
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- Ghiassi, M., Saidane, H., & Zimbra, D. K. (2005). A dynamic artificial neural network model for forecasting time series events. International Journal of Forecasting. https://doi.org/10.1016/j.ijforecast.2004.10.008
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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