- Published
- 2021-06-02
- Venue
- Annals of Data Science, vol. 10, no. 1, pp. 183-208
- Cited by
- 730
- Open access
- No free copy found
- DOI
- 10.1007/s40745-021-00344-x
Cite this paper
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- APA 7
Kurani, A., Doshi, P., Vakharia, A., & Shah, M. (2021). A Comprehensive Comparative Study of Artificial Neural Network (ANN) and Support Vector Machines (SVM) on Stock Forecasting. Annals of Data Science, 10(1), 183–208. https://doi.org/10.1007/s40745-021-00344-x
- MLA 9
Kurani, Akshit, et al. "A Comprehensive Comparative Study of Artificial Neural Network (ANN) and Support Vector Machines (SVM) on Stock Forecasting." Annals of Data Science, vol. 10, no. 1, 2021, pp. 183–208, https://doi.org/10.1007/s40745-021-00344-x.
- Chicago
Kurani, Akshit, Pavan Doshi, Aarya Vakharia, and Manan Shah. "A Comprehensive Comparative Study of Artificial Neural Network (ANN) and Support Vector Machines (SVM) on Stock Forecasting." Annals of Data Science 10, no. 1 (2021): 183–208. https://doi.org/10.1007/s40745-021-00344-x.
- GOST
Kurani A., Doshi P., Vakharia A. [и др.] A Comprehensive Comparative Study of Artificial Neural Network (ANN) and Support Vector Machines (SVM) on Stock Forecasting // Annals of Data Science. — 2021. — Vol. 10, № 1. — P. 183–208. — URL: https://doi.org/10.1007/s40745-021-00344-x
- BibTeX
@article{kurani2021comprehensive, title = {{A Comprehensive Comparative Study of Artificial Neural Network (ANN) and Support Vector Machines (SVM) on Stock Forecasting}}, author = {Akshit Kurani and Pavan Doshi and Aarya Vakharia and Manan Shah}, journal = {Annals of Data Science}, year = {2021}, volume = {10}, number = {1}, pages = {183--208}, doi = {10.1007/s40745-021-00344-x}, url = {https://doi.org/10.1007/s40745-021-00344-x} }- RIS
TY - JOUR AU - Kurani, Akshit AU - Doshi, Pavan AU - Vakharia, Aarya AU - Shah, Manan TI - A Comprehensive Comparative Study of Artificial Neural Network (ANN) and Support Vector Machines (SVM) on Stock Forecasting JO - Annals of Data Science VL - 10 IS - 1 SP - 183 EP - 208 PY - 2021 DA - 2021/06/02 DO - 10.1007/s40745-021-00344-x UR - https://doi.org/10.1007/s40745-021-00344-x ER -
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