Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Forecasting stock market movement direction with support vector machine
Computers and Operations Research
Converging Marriage in Honey-Bees Optimization and Application to Stochastic Dynamic Programming
Journal of Global Optimization
Forecasting financial condition of Chinese listed companies based on support vector machine
Expert Systems with Applications: An International Journal
Chaotic bee colony algorithms for global numerical optimization
Expert Systems with Applications: An International Journal
Electricity price forecasting based on support vector machine trained by genetic algorithm
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Parametric active contour model by using the honey bee mating optimization
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
SMBO: A self-organizing model of marriage in honey-bee optimization
Expert Systems with Applications: An International Journal
Forecasting model selection through out-of-sample rolling horizon weighted errors
Expert Systems with Applications: An International Journal
An efficient CMAC neural network for stock index forecasting
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
Integrated circuit (IC) is a vital component of most electronic commodity. IC manufacturing in Taiwan is booming, with revenues from the ICs industry having grown significantly in the recent years. Given the nature of technology, capital intensity and high value-added, accurate forecasting of IC the industry output can improve the competitivity of IC cooperation. Support vector regression (SVR) is an emerging forecasting scheme that has been successfully adopted in many time-series forecasting areas. Additionally, the data preprocessing procedure and the determination of SVR parameters significantly impact the forecasting accuracy of SVR models. Thus, this work develops a support vector regression model with scaling preprocessing and marriage in honey-bee optimization (SVRSMBO) model to accurately forecast IC industry output. The scaling preprocessing procedure is utilized to lower the fluctuation of input data, and the marriage in honey-bees optimization (MBO) algorithm is adopted to determine the three parameters of the SVR model. Numerical data collected from the previous literature are used to demonstrate the performance of the proposed SVRSMBO model. Simulation results indicate that the SVRSMBO model outperforms other forecasting models. Hence, the SVRSMBO model is a promising means of forecasting IC industry output.