台灣是世界排名第一的主機板與筆記型電腦生產國家, 其在2011的佔有率分別為80.4% 與93.6%.而其未來之預測發展對高階主管而言是非常重要. 本研究目的即是提供有效預測模式給主機板,筆記型電腦製造商與相關產業作為未來擴產與投資之重要參考. 在本研究中, 我們運用支持向量回歸與貝氏擴散模式, 分析1998年至2012年台灣主機板與筆記型電腦季出貨之資料, 運用格子點參數搜尋,非線性最小平分法, 基因演算法與粒子群最佳化,分別找出支持向量回歸與貝氏擴散模式之最佳參數. 而運用平均絕對值百分比誤差(Mean Absolute Percentage Error, 簡稱MAPE)進行預測效益評估.本研究結果分析,支持向量回歸之平均絕對值百分比誤差低於貝氏擴散模式,可運用在主機板與筆記型電腦市場預測分析.

Taiwan is the world’s leading motherboard (MB) and notebook (NB) manufacturer, boasting a 2011 global market share of 80.4% and 93.6%, respectively. It is highly crucial for executives to predict future trends from within an environment of uncertainty. The aim of this study is to provide an efficient forecasting model to serve as a key reference for MB and NB manufacturers looking to expand or invest. We propose the following 2 forecasting models based on MB and NB quarterly shipment data from 1998-2012: (a) support vector regression (SVR) using a grid search method for the estimation of three parameters; and (b) Bass diffusion models (BDMs) using non-linear least square (NLS), genetic algorithm (GA), and particle swarm optimization (PSO) methods for parameter optimization. We also evaluate the forecasting accuracy by actual mean absolute percentage error (MAPE).The obtained MAPE values indicate that the proposed SVR model outperforms the BDMs using NLS, GA, and PSO for fitting and forecasting based on MAPE, and is therefore recommended for MB and NB market forecasting analysis.

1.Gfk Boutique Research & IEK, (2012). Annual Report. Gfk SE, Nuremberg.
2.Yang, F.L., (2012). Semiconductor Industry Yearbook. Taiwan: Industrial Technology Research (ITRI).
3.Bass, F.M., (2004). Comment on: A new product growth model for consumer durables. Management Science, 50(12), 1833-1840.
4.Golder, P.N. and Tellus, G.J., (1998). Beyond diffusion: An affordability model of the growth of new consumer durables. Journal of Forecasting, 17, 259-280.
5.Hsiao, Y.Y. and Wang, F.K., (2012). Forecasting analysis for global copper clad laminate market. Journal of Convergence Information Technology, 7(3), 250-258.
6.Wu, F.S. and Chu, W.L., (2010). Diffusion models of mobile telephone. Journal of Business Research, 63, 497-501.
7.Tseng, F.M. and Hu, Y.C., (2009). Quadratic-interval bass model for new products sales diffusion. Expert Systems with Applications, 36, 8496-8502.
8.Tsai, B.H., Li, Y. and Lee, G.H., (2010). Forecasting global adoption of crystal display televisions with modified product diffusion model, Computer and Industrial Engineering, 58, 553-562.
9.Wang, F.K. and Chang, K.K., (2010). Adaptive neuro-fuzzy inference system for combined forecasts in a panel manufacturer, Expert Systems with Applications, 37, 8110-8126.
10.Chu, C.P. and Pan, J.G., (2008). The forecasting of the mobile Internet in Taiwan by diffusion model, Technological Forecasting and Social Change, 75. 1054-1067.
11.Chang, S.C., Lai, H.C. and Yu, H.C., (2005). A variable p value rolling grey forecasting model for taiwan semiconductor industry production. Technological Forecasting & Social Change, 72, 623-640.
12.Schmittelin, D.C. and Mahajan, V., (1982). Maximum likehood Estimation for An Innovation Diffusion Model of New Products, Market Science, 1(1), 57-78.
13.Srinivasan, V. and Mason, C.H., (1986). Nonlinear least squares estimation of new product diffusion models, Market Science, 5(2), 169-178.
14.Venkatesan, R. and Kumar, V., (2002). A genetic algorithms approach to growth phase forecasting of wireless subscribers, International Journal of Forecasting, 18, 625-646.
15.Holland, J.H.,(1975). Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor, Michigan.
16.Kennedy, J. and Eberhart, R., (1995). Particle swarm optimization. In proceeding of IEEE international conference on neural networks, 4, 1942-1948.
17.Kuo, I.H., Horng, S, J., Chen, Y.H., Run R.S., Kao, T.W., Chen, R.J., Lai, J.L. and Lin, T.L., (2010). Forecasting TAIFEX based on fuzzy time series and particle swarm optimization, Expert Systems with Application, 37, 1494-1502.
18.Lim, T.L., Horng. S.J., Kao, T.W., Chen, Y.H., Run, R.S., Chen, R.J., Lai, J.L. and Kuo, I.H., (2010). An efficient job-shop scheduling algorithm based on particle swarm optimization, Expert Systems with Application, 37, 2629-2636.
19.Vapnik, V., (1995).The Nature of Statistic Learning, Springer-Verlag, New York.
20.Tay, E. H. and Cao, L., (2001). Application of support vector machines in financial time series forecasting, Omega, 29, 309-317.
21.Wang, W., Xu, Z. and Lu., J.W., (2003) . Three improved neural network models for air quality forecasting, Engineering Computations, 20, 192-210.
22.Tay, E. H. and Cao., L., (2002) . Modified support vector machines in financial time series forecasting. Neurocomputing , 48, 847-861.
23.Cao, L., (2003). Support vector machines experts for time series forecasting, Neurocomputing, 51, 321-339.
24.Pai, P.F. and Hong, W.C., (2005). Support vector machines with simulated annealing algorithms in electricity load forecasting, Energy Conversion and Management, 46, 2669-2688.
25.Chen, K. Y. and Wang, C. H., (2007). Support vector regression with genetic algorithms in forecasting tourism demand, Tourism Management, 28, 215-226.
26.Hong, W. C., (2009). Electric load forecasting by support vector model, Applied Mathematical Modeling, 33, 2444-2454.
27.Hong, W.C., Dong, Y.C., Chen, L.Y., and Lai, C. Y., (2010). Taiwanese 3G mobile phone demand forecasting by SVR with hybrid evolutionary algorithms. Expert Systems with Applications, 37, 4452-4462.
28.Bass, F.M., (1969). A modified product growth model for consumer durable. Management Science, 15(5), 215-217.
29.Clerc, M. and Kennedy, J., (2002).The particle swarm-explosion, stability, and convergence in a multidimensional complex space, IEEE Transactions on Evolutionary Computation, 6 , 58-73.
30.Trontl, K., Smuc,T., and Pevec, D., (2007). Support vector regression model for the estimation of γ-ray buildup factors for multi-layer shields. Annals of Nuclear Energy, 34, 932-952.
31.Hong, W.C., (2009). Hybrid evolutionary algorithm in SVR-based electric loading forecasting model. Electrical Power and Energy Systems, 31, 409-417.
32.Wilson, J.H. and Keating, B., (2009). Business Forecasting with Forecast X. Sixth Edition. MA: Mc-Graw Hill , Boston.
33.MIC, (2013). 1998-2012 Quarterly Report, Institute for information industry, Taipei.