Stock index forecast is regarded as a challenging task of financial time-series prediction. In this paper, the non-linear support vector regression (SVR) method was optimized for the application in stock index predict...Stock index forecast is regarded as a challenging task of financial time-series prediction. In this paper, the non-linear support vector regression (SVR) method was optimized for the application in stock index prediction. The parameters (C, σ) of SVR models were selected by three different methods of grid search (GRID), particle swarm optimization (PSO) and genetic algorithm (GA).The optimized parameters were used to predict the opening price of the test samples. The predictive results shown that the SVR model with GRID (GRID-SVR), the SVR model with PSO (PSO-SVR) and the SVR model with GA (GA-SVR) were capable to fully demonstrate the time-dependent trend of stock index and had the significant prediction accuracy. The minimum root mean square error (RMSE) of the GA-SVR model was 15.630, the minimum mean absolute percentage error (MAPE) equaled to 0.39% and the correspondent optimal parameters (C, σ) were identified as (45.422, 0.012). The appreciated modeling results provided theoretical and technical reference for investors to make a better trading strategy.展开更多
Health care is an area in which all who are involved should try to ensure that the quality of the provision is at a high level, especially in maternity wards. The quality of these services is not only the competence o...Health care is an area in which all who are involved should try to ensure that the quality of the provision is at a high level, especially in maternity wards. The quality of these services is not only the competence of the medical staff, but the whole set of factors prevailing in the ward and in the hospital, from cleanliness of the delivery room to the staff courtesy of the institution. The article presents the results of research on clients＇ satisfaction with the quality of the services offered by the selected maternity ward in Provincial Hospital in Bielsko-Biala. Customer Satisfaction Index （CSI） method was used for analysis.展开更多
文摘Stock index forecast is regarded as a challenging task of financial time-series prediction. In this paper, the non-linear support vector regression (SVR) method was optimized for the application in stock index prediction. The parameters (C, σ) of SVR models were selected by three different methods of grid search (GRID), particle swarm optimization (PSO) and genetic algorithm (GA).The optimized parameters were used to predict the opening price of the test samples. The predictive results shown that the SVR model with GRID (GRID-SVR), the SVR model with PSO (PSO-SVR) and the SVR model with GA (GA-SVR) were capable to fully demonstrate the time-dependent trend of stock index and had the significant prediction accuracy. The minimum root mean square error (RMSE) of the GA-SVR model was 15.630, the minimum mean absolute percentage error (MAPE) equaled to 0.39% and the correspondent optimal parameters (C, σ) were identified as (45.422, 0.012). The appreciated modeling results provided theoretical and technical reference for investors to make a better trading strategy.
文摘Health care is an area in which all who are involved should try to ensure that the quality of the provision is at a high level, especially in maternity wards. The quality of these services is not only the competence of the medical staff, but the whole set of factors prevailing in the ward and in the hospital, from cleanliness of the delivery room to the staff courtesy of the institution. The article presents the results of research on clients＇ satisfaction with the quality of the services offered by the selected maternity ward in Provincial Hospital in Bielsko-Biala. Customer Satisfaction Index （CSI） method was used for analysis.
