The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Carbon price forecasting with a novel hybrid ARIMA and least squares support vector machines methodology
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Citations
Cites
Kennedy, J., & Eberhart, R. (2002) · Proceedings of ICNN'95 - International Conference on Neural Networks · doi:10.1109/icnn.1995.488968 · Cited by 58,192
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
Libsvm : A library for support vector machines
Ferrari, V. (2008) · Medical Entomology and Zoology · Cited by 10,122
Least Squares Support Vector Machine Classifiers
Suykens, J. A. K., & Vandewalle, J. (1999) · Neural Processing Letters · doi:10.1023/a:1018628609742 · Cited by 9,362
The Nature of Statistical Learning Theory
Sain, S. R. (1996) · Technometrics · doi:10.1080/00401706.1996.10484565 · Cited by 5,434
Diebold, F., & Mariano, R. (1994) · National Bureau of Economic Research · doi:10.3386/t0169 · Cited by 5,185
Time series forecasting using a hybrid ARIMA and neural network model
Zhang, G. P. (2003) · Neurocomputing · doi:10.1016/s0925-2312(01)00702-0 · Cited by 4,428
Support Vector Method for Function Approximation, Regression Estimation, and Signal Processing
Vapnik, V. N., Golowich, S. E., & Smola, A. J. (1997) · Cited by 2,757
A hybrid ARIMA and support vector machines model in stock price forecasting
Pai, P.-F., & Lin, C.-S. (2005) · Omega · doi:10.1016/j.omega.2004.07.024 · Cited by 998
Cited by
Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336
Zhu, B., Han, D., Wang, P., Wu, Z., Zhang, T., & Wei, Y.-M. (2017) · Applied Energy · doi:10.1016/j.apenergy.2017.01.076 · Cited by 307
Self-citationA novel hybrid method for crude oil price forecasting
Zhang, J.-L., Zhang, Y.-J., & Zhang, L. (2015) · Energy Economics · doi:10.1016/j.eneco.2015.02.018 · Cited by 272
A novel multiscale nonlinear ensemble leaning paradigm for carbon price forecasting
Zhu, B., Ye, S., Wang, P., He, K., Zhang, T., & Wei, Y.-M. (2018) · Energy Economics · doi:10.1016/j.eneco.2017.12.030 · Cited by 236
Self-citationSun, W., & Zhang, C. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.09.118 · Cited by 181
Metadata
- Authors
- Zhu, B., & Wei, Y.
- Venue
- Omega
- Publisher
- Elsevier BV
- Published
- 2013-06
- Volume
- 41
- Issue
- 3
- Pages
- 517-524
- ISSN
- 0305-0483 (ISSN-L)
- OpenAlex
- W2028068740
- Cited by
- 374
- References
- 30
- FWCI
- 13.93
- Type
- journal article
- Language
- en
- Open access
- closed
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- Zhu, B., & Wei, Y. (2013). Carbon price forecasting with a novel hybrid ARIMA and least squares support vector machines methodology. Omega. https://doi.org/10.1016/j.omega.2012.06.005
Data sources
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.
