Search Results - subject:"support vector regression"

Liao, Zhiqiang; Dai, Sheng; Kuosmanen, Timo - In: European journal of operational research : EJOR 313 (2024) 3, pp. 858-870

Persistent link: https://www.econbiz.de/10014456645

Modeling the effect of population size on banking transaction channels in Nigeria: Grey box vs Support Vector Regression

Bartholomew, Desmond C.; Olewuezi, Ngozi P.; Nwaigwe, … - In: CBN Journal of Applied Statistics 15 (2024) 1, pp. 1-31

. The results show that the Support Vector Regression (SVR) model best fits the ATM, Online, and USSD channels of …

Persistent link: https://www.econbiz.de/10015396511

Modeling the effect of population size on banking transaction channels in Nigeria : Grey box vs Support Vector Regression

Bartholomew, Desmond C.; Olewuezi, Ngozi P.; Nwaigwe, … - In: CBN journal of applied statistics 15 (2024) 1, pp. 1-31

. The results show that the Support Vector Regression (SVR) model best fits the ATM, Online, and USSD channels of …

Persistent link: https://www.econbiz.de/10015393855

A hybrid model for forecasting realized volatility based on heterogeneous autoregressive model and support vector regression

Zhuo, Yue; Morimoto, Takayuki - In: Risks : open access journal 12 (2024) 1, pp. 1-16

vector regression (SVR) model to forecast realized volatility (RV). The first model is a residual-type model, where the RV is …In this study, we proposed two types of hybrid models based on the heterogeneous autoregressive (HAR) model and support …

Persistent link: https://www.econbiz.de/10014480965

A comparative assessment of machine learning methods for predicting housing prices using Bayesian optimization

Lahmiri, Salim; Bekiros, Stelios; Avdoulas, Christos - In: Decision analytics journal 6 (2023), pp. 1-8

, followed by Gaussian process regression and support vector regression. In addition, all three aforementioned predictive systems … statistical models to house price prediction problems. In that order, we use boosting ensemble regression trees, support vector … regression, and Gaussian process regression. Bayesian optimization is implemented in a ten-fold cross-validation framework to …

Persistent link: https://www.econbiz.de/10014505865

Financial technology as a driver of poverty alleviation in China: Evidence from an innovative regression approach

Ye, Yafen; Chen, Shenglan; Li, Chunna - In: Journal of Innovation & Knowledge (JIK) 7 (2022) 1, pp. 1-12

Despite the booming development of financial technology (fintech) in China, little academic literature has explored the effects of fintech on poverty. This paper explores the effects of fintech on poverty alleviation in the provinces of China. The sample includes data for 31 provinces for the...

Persistent link: https://www.econbiz.de/10013266814

Financial technology as a driver of poverty alleviation in China : evidence from an innovative regression approach

Ye, Yafen; Chen, Shenglan; Li, Chunna - In: Journal of innovation & knowledge : JIK 7 (2022) 1, pp. 1-12

Persistent link: https://www.econbiz.de/10013166594

American option pricing with machine learning : an extension of the Longstaff-Schwartz method

Lin, Jingying; Almeida, Caio - In: Revista Brasileira de Finanças : RBFin 19 (2021) 3, pp. 85-109

Persistent link: https://www.econbiz.de/10012804834

A machine learning approach to univariate time series forecasting of quarterly earnings

Fischer, Jan Alexander; Pohl, Philipp; Ratz, Dietmar - In: Review of Quantitative Finance and Accounting 55 (2020) 4, pp. 1163-1179

We propose our quarterly earnings prediction (QEPSVR) model, which is based on epsilon support vector regression (ε …

Persistent link: https://www.econbiz.de/10014504255

China's missing pigs : correcting China's hog inventory data using a machine learning approach

Shao, Yongtong; Li, Minghao; Hayes, Dermot J.; Zhang, … - 2020

Persistent link: https://www.econbiz.de/10012387933