STOCK PREDICTION AND SIMULATION OF TRADE USING SUPPORT VECTOR REGRESSION
نویسندگان
چکیده
منابع مشابه
Prediction of daily evaporation using hybrid support vector regression-firefly optimization algorithm and multilayer perceptron
Prediction of daily evaporation is a valuable and determinant tool in sustainable agriculture and hydrological issues, especially in the design and management of water resources systems. Therefore, in this study, the ability of artificial intelligence models of multi-layer perceptron (MLP), support vector regression (SVR), and the hybrid model of support vector regression-firefly optimization a...
متن کاملStock Market Simulation using Support Vector Machines
The aim of this research is to analyse the different results that can be achieved using Support Vector Machines (SVM) to forecast the weekly change movement of the different simulated markets. The different simulated markets are developed by a GARCH model based on the S&P 500. These simulated markets are grouped by a main parameter: high volatility, bearish trend, bullish trend and low volatili...
متن کاملSupport vector regression for prediction of gas reservoirs permeability
Reservoir permeability is a critical parameter for characterization of the hydrocarbon reservoirs. In fact, determination of permeability is a crucial task in reserve estimation, production and development. Traditional methods for permeability prediction are well log and core data analysis which are very expensive and time-consuming. Well log data is an alternative approach for prediction of pe...
متن کاملSupport Vector Machine Regression for Volatile Stock Market Prediction
Recently, Support Vector Regression (SVR) has been introduced to solve regression and prediction problems. In this paper, we apply SVR to financial prediction tasks. In particular, the financial data are usually noisy and the associated risk is time-varying. Therefore, our SVR model is an extension of the standard SVR which incorporates margins adaptation. By varying the margins of the SVR, we ...
متن کاملThe Porosity Prediction of One of Iran South Oil Field Carbonate Reservoirs Using Support Vector Regression
Porosity is considered as an important petrophysical parameter in characterizing reservoirs, calculating in-situ oil reserves, and production evaluation. Nowadays, using intelligent techniques has become a popular method for porosity estimation. Support vector machine (SVM) a new intelligent method with a great generalization potential of modeling non-linear relationships has been introduced fo...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Research in Engineering and Technology
سال: 2018
ISSN: 2321-7308,2319-1163
DOI: 10.15623/ijret.2018.0704009