The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Neural network ensemble operators for time series forecasting
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Citations
Cites
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
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
Another look at measures of forecast accuracy
Hyndman, R. J., & Koehler, A. B. (2006) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2006.03.001 · Cited by 5,757
Hansen, L. K., & Salamon, P. (1990) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.58871 · Cited by 4,260
Bates, J. M., & Granger, C. W. J. (1969) · Journal of the Operational Research Society · doi:10.1057/jors.1969.103 · Cited by 3,201
Kernel density estimation via diffusion
Botev, Z. I., Grotowski, J. F., & Kroese, D. P. (2010) · The Annals of Statistics · doi:10.1214/10-aos799 · Cited by 1,689
Recurrent neural networks and robust time series prediction
Connor, J. T., Martin, R. D., & Atlas, L. E. (1994) · IEEE Transactions on Neural Networks · doi:10.1109/72.279188 · Cited by 1,316
Neural network load forecasting with weather ensemble predictions
Taylor, J. W., & Buizza, R. (2002) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2002.800906 · Cited by 459
Simple robust averages of forecasts: Some empirical results
Jose, V. R. R., & Winkler, R. L. (2008) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2007.06.001 · Cited by 242 · cited 2×
“Jose and Winkler (2008) using a similar argument advocated the use of trimmed and winsorised means.”
Background
Cited by
Statistical and machine learning forecasting methods: Concerns and ways forward
Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2018) · PLOS ONE · doi:10.1371/journal.pone.0194889 · Cited by 1,528
M5 accuracy competition: Results, findings, and conclusions
Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2022) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2021.11.013 · Cited by 554
An Experimental Review on Deep Learning Architectures for Time Series Forecasting
Lara-Benítez, P., Carranza-García, M., & Riquelme, J. C. (2021) · International Journal of Neural Systems · doi:10.1142/s0129065721300011 · Cited by 532
Bandara, K., Bergmeir, C., & Smyl, S. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.112896 · Cited by 435
Bagging exponential smoothing methods using STL decomposition and Box–Cox transformation
Bergmeir, C., Hyndman, R. J., & Benítez, J. M. (2016) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2015.07.002 · Cited by 328
Metadata
- Authors
- Kourentzes, N., Barrow, D. K., & Crone, S. F.
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2014-07
- Volume
- 41
- Issue
- 9
- Pages
- 4235-4244
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W2149905014
- Cited by
- 292
- References
- 9
- FWCI
- 24.64
- Type
- journal article
- Language
- en
- Open access
- green
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- Kourentzes, N., Barrow, D. K., & Crone, S. F. (2014). Neural network ensemble operators for time series forecasting. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2013.12.011
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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.
