scholarly journals Predicting the Istanbul Stock Exchange Index Return using Technical Indicators

2013 ◽  
Vol 2 (3) ◽  
pp. 111-117
Author(s):  
Senol Emir
Keyword(s):  
Stock Exchange ◽  
Absolute Error ◽  
Percentage Error ◽  
Support Vector ◽  
Model Parameters ◽  
Technical Indicators ◽  
Istanbul Stock Exchange ◽  
Vector Machines ◽  
Classical Linear Regression ◽  
Daily Returns

The aim of this study to examine the performance of Support Vector Regression (SVR) which is a novel regression method based on Support Vector Machines (SVM) approach in predicting the Istanbul Stock Exchange (ISE) National 100 Index daily returns. For bechmarking, results given by SVR were compared to those given by classical Linear Regression (LR). Dataset contains 6 technical indicators which were selected as model inputs for 2005-2011 period. Grid search and cross valiadation is used for finding optimal model parameters and evaluating the models. Comparisons were made based on Root Mean Square (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Theil Inequality Coefficient (TIC) and Mean Mixed Error (MME) metrics. Results indicate that SVR outperforms the LR for all metrics.

scholarly journals Applying Least Squares Support Vector Machines to Mean-Variance Portfolio Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Jian Wang ◽  
Junseok Kim
Keyword(s):  
Support Vector Machines ◽  
Least Squares ◽  
Portfolio Management ◽  
Stock Exchange ◽  
Efficient Frontier ◽  
Support Vector ◽  
Research Fields ◽  
Vector Machines ◽  
Mean Variance Portfolio ◽  
Mean Variance

Portfolio selection problem introduced by Markowitz has been one of the most important research fields in modern finance. In this paper, we propose a model (least squares support vector machines (LSSVM)-mean-variance) for the portfolio management based on LSSVM. To verify the reliability of LSSVM-mean-variance model, we conduct an empirical research and design an algorithm to illustrate the performance of the model by using the historical data from Shanghai stock exchange. The numerical results show that the proposed model is useful when compared with the traditional Markowitz model. Comparing the efficient frontier and total wealth of both models, our model can provide a more measurable standard of judgment when investors do their investment.

Coke Rate Prediction Model Based on Genetic Algorithm Optimized Support Vector Machine

2015 ◽  
Vol 740 ◽  
pp. 600-603
Author(s):  
You Jun Yue ◽  
Yan Fei Hu ◽  
Hui Zhao ◽  
Hong Jun Wang
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Absolute Error ◽  
Support Vector ◽  
Model Parameters ◽  
Coke Rate ◽  
Rate Model ◽  
Iron And Steel ◽  
Model Based ◽  
Blast Furnace Coke

The accurate prediction model’s establishing of the blast furnace coke rate is important for optimizing the integrated production indicators of iron and steel enterprise. For the problem of accuracy of the model of coke rate, This paper established blast coke rate modeling with support vector machine algorithm, the model parameters of support vector machine was optimized by genetic algorithm, then a coke rate model based on support vector machine with the best parameters was built. Simulation results showed that: the forecasting model’s outcome, average absolute error and the mean relative error, was small which is based on genetic algorithm optimized SVM. coke rate model based on Genetic algorithm optimized support vector machine has high degree of accuracy and a certain practicality.

scholarly journals Visualization & Prediction of COVID-19 Future Outbreak by Using Machine Learning

2021 ◽  
Vol 13 (3) ◽  
pp. 16-32
Author(s):  
Ahmed Hassan Mohammed Hassan ◽  
◽  
Arfan Ali Mohammed Qasem ◽  
Walaa Faisal Mohammed Abdalla ◽  
Omer H. Elhassan
Keyword(s):  
Machine Learning ◽  
Polynomial Regression ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Future Perspective ◽  
Support Vector ◽  
Squared Error ◽  
Vector Machines ◽  
The World ◽  
Negative Factors

Day by day, the accumulative incidence of COVID-19 is rapidly increasing. After the spread of the Corona epidemic and the death of more than a million people around the world countries, scientists and researchers have tended to conduct research and take advantage of modern technologies to learn machine to help the world to get rid of the Coronavirus (COVID-19) epidemic. To track and predict the disease Machine Learning (ML) can be deployed very effectively. ML techniques have been anticipated in areas that need to identify dangerous negative factors and define their priorities. The significance of a proposed system is to find the predict the number of people infected with COVID19 using ML. Four standard models anticipate COVID-19 prediction, which are Neural Network (NN), Support Vector Machines (SVM), Bayesian Network (BN) and Polynomial Regression (PR). The data utilized to test these models content of number of deaths, newly infected cases, and recoveries in the next 20 days. Five measures parameters were used to evaluate the performance of each model, namely root mean squared error (RMSE), mean squared error (MAE), mean absolute error (MSE), Explained Variance score and r2 score (R2). The significance and value of proposed system auspicious mechanism to anticipate these models for the current cenario of the COVID-19 epidemic. The results showed NN outperformed the other models, while in the available dataset the SVM performs poorly in all the prediction. Reference to our results showed that injuries will increase slightly in the coming days. Also, we find that the results give rise to hope due to the low death rate. For future perspective, case explanation and data amalgamation must be kept up persistently.

scholarly journals Earthquake Time Prediction using CatBoost and SVR

2019 ◽  
Vol 9 (1) ◽  
pp. 225-229
Keyword(s):  
Support Vector Machine ◽  
Cross Validation ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Acoustic Signals ◽  
High Accuracy ◽  
Support Vector ◽  
Time Prediction ◽  
Vector Machines ◽  
Time Of Failure

Seismic tremors everywhere throughout the globe have been a noteworthy reason for decimation and death toll and property. The following context expects to recognize earthquakes at a beginning time utilizing AI. This will help individuals and salvage groups to make their errand simpler. The information in this manner comprises of these seismic acoustic signals and the time of failure. The model is then prepared utilizing the CatBoost model and the utilization of Support Vector Machines. This will help foresee the time at which a Seismic tremor may happen. CatBoost Regression Algorithm gives a Mean Absolute Error of about 1.860. The Cross Validation (CV) Score for the Support Vector Machine (SVM) approach is -2.1651. The datasets metrics are not reliable on any outer parameter in this manner the variety of exactness is constrained, and high accuracy is accomplished.

Evapotranspiration Estimation Using Support Vector Machines and Hargreaves-Samani Equation for St. Johns, FL, USA

2017 ◽  
Author(s):  
Fatih Ünes ◽  
Yunus Ziya Kaya ◽  
Mustafa Mamak ◽  
Mustafa Demirci
Keyword(s):  
Support Vector Machines ◽  
Empirical Equation ◽  
Hydrological Cycle ◽  
Nonlinear Problems ◽  
Absolute Error ◽  
Support Vector ◽  
Data Set ◽  
Determination Coefficient ◽  
Vector Machines ◽  
Evapotranspiration Estimation

Information about Evapotranspiration (ET) calculations are not clear enough even it is an important part of hydrological cycle. There are many parameters which effect ET directly or indirectly such as Solar Radiation (SR) and Air Temperature (AT). In this study authors focused on the modelling ET using Support Vector Machines (SVM) method because this method has abilities to solve nonlinear problems. For the training SVM 1158 daily AT, SR, Wind Speed (U) and Relative Humidity (RH) meteorological parameters are used and model is tested using 385 daily parameters. Data set is taken from St. Johns, Florida, USA weather station. To understand the abilities of SVM for ET prediction against Hargreaves-Samani formula, the test set is applied to this empirical equation. Determination coefficient of SVM with observed daily ET values is calculated as 0.913 and determination coefficient of Hargreaves- Samani formula with observed daily ET is found as 0.910. Comparison between both methods is done using Mean Square Error (MSE), Mean Absolute Error (MEA) and determination coefficient statistics. As a result it is seen that SVM method is trustier than Hargreaves-Samani formula for daily ET prediction.

SVR-Based Predictive Model for 2519 Aluminum Alloy

2016 ◽  
Vol 719 ◽  
pp. 41-45 ◽  
Author(s):  
J.L. Tang ◽  
H.Y. Liu ◽  
M.H. Gui ◽  
J.Y. Tang
Keyword(s):  
Aluminum Alloy ◽  
Flow Stress ◽  
Absolute Error ◽  
Multiple Correlation Coefficient ◽  
Influential Factors ◽  
Percentage Error ◽  
Support Vector ◽  
Strain And Strain Rate ◽  
Alloy Base ◽  
Dynamical Parameters

For 2519 aluminum alloy, there are very complex nonlinear relations among the thermal dynamical parameters in the process of deforming. In this paper, the support vector regression (SVR) approach is proposed to establish a model for predicting flow stress of 2519 alloy base on the flows tress experimental data of 2519 aluminum alloy under two influential factors, including strain and strain rate. Research showed that the prediction precision of SVR model is high enough: the mean absolute error (MAE) is 0.181, mean absolute percentage error (MAPE) is 0.434%, root mean square error (RMSE) is 0.22, multiple correlation coefficient (R2) is 0.998. This research suggests that SVR is an effective and powerful tool for predicting the flow stress of 2519 aluminum alloy.

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