scholarly journals Prediction of penetration rate by Coupled Simulated Annealing-Least Square Support Vector Machine (CSA_LSSVM) learning in a hydrocarbon formation based on drilling parameters

2021 ◽  
Vol 7 ◽  
pp. 3971-3978
Author(s):  
Heng Chen ◽  
Jinying Duan ◽  
Rui Yin ◽  
Vadim V. Ponkratov ◽  
John William Grimaldo Guerrero
Keyword(s):  
Support Vector Machine ◽  
Simulated Annealing ◽  
Penetration Rate ◽  
Least Square ◽  
Support Vector ◽  
Drilling Parameters ◽  
Hydrocarbon Formation ◽  
Coupled Simulated Annealing

Optimization of Financial Forecasting Based on Least Squares Support Vector Machine

2013 ◽  
Vol 760-762 ◽  
pp. 1987-1991
Author(s):  
Yun Fa Li
Keyword(s):  
Support Vector Machine ◽  
Simulated Annealing ◽  
Stock Market ◽  
Prediction Model ◽  
Least Squares ◽  
Simulated Annealing Algorithm ◽  
Least Square ◽  
Support Vector ◽  
Stock Investment ◽  
Annealing Algorithm

To master the variation regularity of finance, obtain greater benefits in stock investment. study of the support vector machine and application in prediction of stock market. The simulated annealing algorithm to optimize the least squares support vector machine prediction model, and the least square support vector machine and simulated annealing algorithm is described, given the optimal prediction model. Through the research on the simulation of the Hang Seng Index, shows that this method is simple, fast convergence, the algorithm with high accuracy. Has the actual guiding sense for investors, the stock market of the financial firm to operate.

Sign in / Sign up

Export Citation Format

Share Document