Time-series analysis is important to a wide range of disciplines transcending both the physical and social sciences for proactive policy decisions. Statistical models have sound theoretical basis and have been success...Time-series analysis is important to a wide range of disciplines transcending both the physical and social sciences for proactive policy decisions. Statistical models have sound theoretical basis and have been successfully used in a number of problem domains in time series forecasting. Due to power and flexibility, Box-Jenkins ARIMA model has gained enormous popularity in many areas and research practice for the last three decades. More recently, the neural networks have been shown to be a promising alternative tool for modeling and forecasting owing to their ability to capture the nonlinearity in the data. However, despite the popularity and the superiority of ARIMA and ANN models, the empirical forecasting performance has been rather mixed so that no single method is best in every situation. In this study, a hybrid ARIMA and neural networks model to time series forecasting is proposed. The basic idea behind the model combination is to use each model’s unique features to capture different patterns in the data. With three real data sets, empirical results evidently show that the hybrid model outperforms ARIMA and ANN model noticeably in terms of forecasting accuracy used in isolation.展开更多
Development of effective chromatographic or electrophoretic separation involves judicious deciding of selection of optimal experimental conditions that can provide an adequate resolution at a reasonable run time for t...Development of effective chromatographic or electrophoretic separation involves judicious deciding of selection of optimal experimental conditions that can provide an adequate resolution at a reasonable run time for the separation of interested components. Box-Behnken factorial design was effectively applied for the separation optimization of eight structurally related sulfonamides using capillary zone electrophorosis and reverse high performance liquid chromatography. Optimum values for volume ratio of THF to H2O in eluent, column temperature and flow rate of eluent are found as 12 to 88, 35℃ and 1.0 mL/min, respectively. Box-Behnken modified optimization model is extended to separation by capillary electrophoresis (CE). While using CE, a satisfactory separation is achieved with a minimum resolution larger than 1.0 for a separation time less than 10 min.展开更多
文摘Time-series analysis is important to a wide range of disciplines transcending both the physical and social sciences for proactive policy decisions. Statistical models have sound theoretical basis and have been successfully used in a number of problem domains in time series forecasting. Due to power and flexibility, Box-Jenkins ARIMA model has gained enormous popularity in many areas and research practice for the last three decades. More recently, the neural networks have been shown to be a promising alternative tool for modeling and forecasting owing to their ability to capture the nonlinearity in the data. However, despite the popularity and the superiority of ARIMA and ANN models, the empirical forecasting performance has been rather mixed so that no single method is best in every situation. In this study, a hybrid ARIMA and neural networks model to time series forecasting is proposed. The basic idea behind the model combination is to use each model’s unique features to capture different patterns in the data. With three real data sets, empirical results evidently show that the hybrid model outperforms ARIMA and ANN model noticeably in terms of forecasting accuracy used in isolation.
基金Project(20235010) support by the NSFC-KOSEF Scientific Cooperation ProgramProject supported by the Program for New Century Talents of University in Henan ProvinceProgram for Backbone Teacher in Henan Province, China
文摘Development of effective chromatographic or electrophoretic separation involves judicious deciding of selection of optimal experimental conditions that can provide an adequate resolution at a reasonable run time for the separation of interested components. Box-Behnken factorial design was effectively applied for the separation optimization of eight structurally related sulfonamides using capillary zone electrophorosis and reverse high performance liquid chromatography. Optimum values for volume ratio of THF to H2O in eluent, column temperature and flow rate of eluent are found as 12 to 88, 35℃ and 1.0 mL/min, respectively. Box-Behnken modified optimization model is extended to separation by capillary electrophoresis (CE). While using CE, a satisfactory separation is achieved with a minimum resolution larger than 1.0 for a separation time less than 10 min.
