Zhou Hong
Faculty of Computer Engineering, Huaiyin Institute of Technology, 223005, Huaian, China
Zhu Quanyin
Faculty of Computer Engineering, Huaiyin Institute of Technology, 223005, Huaian, China
Li Xiang
Faculty of Computer Engineering, Huaiyin Institute of Technology, 223005, Huaian, China
ABSTRACT
Computer market becomes more sophisticated and price forecasting is gaining importance for market participants to adjust their market behavior. In the past, price forecasting of computer market is mainly concentrated in the forecasting of the computer itself such as desktops, laptops and so on. Whereas a contrast resolution is proposed to deal with this issue that forecasts price trends of computer market through price forecasting of main computer accessories which can make the forecasting window in advance. Two classic model, Back Propagation (BP) neural network and Support Vector Machine (SVM), are introduced to implement the week-ahead price forecasting of computer accessories. The simulation results show that SVM model is better than BP neural network model for its higher forecasting accuracy. Under the same forecasting conditions, the Relative Errors of SVM model is 1.87% lower than that of BP NN model and the mean absolute errors of SVM model is 17.91% lower than that of BP NN model. Therefore, price forecasting for computer accessories based on SVM is valuable and feasible for computer market which can provide richer and more accurate analysis information of price trends for market participants in advance, with a high reference value.
PDF References Citation
How to cite this article
Zhou Hong, Zhu Quanyin and Li Xiang, 2013. Week-ahead Price Forecasting of Computer Accessories Based on BP and SVM. Information Technology Journal, 12: 4937-4945.
DOI: 10.3923/itj.2013.4937.4945
URL: https://scialert.net/abstract/?doi=itj.2013.4937.4945
DOI: 10.3923/itj.2013.4937.4945
URL: https://scialert.net/abstract/?doi=itj.2013.4937.4945
REFERENCES
- Catalao, J.P.S., H.M.I. Pousinho and V.M.F. Mendes, 2011. Hybrid wavelet-PSO-ANFIS approach for short-term electricity prices forecasting. IEEE Trans. Power Syst., 26: 137-144.
CrossRef - Chang, P.C. and C.Y. Fan, 2008. A hybrid system integrating a wavelet and TSK fuzzy rules for stock price forecasting. IEEE Trans. Syst. Man Cybernetics Part C: Appl. Rev., 38: 802-815.
CrossRef - Chen, X., Z.Y. Dong, K. Meng, Y. Xu, K.P. Wong and Ngan, 2012. Electricity price forecasting with extreme learning machine and bootstrapping. IEEE Trans. Power Syst., 27: 2055-2062.
CrossRef - Gonzalez, V., J. Contreras and D.W. Bunn, 2012. Forecasting power prices using a hybrid fundamental-econometric Model. IEEE Trans. Power Syst., 27: 363-372.
CrossRef - Gradojevic, N. and R. Gencay, 2011. Financial applications of nonextensive entropy. IEEE Signal Process. Mag., 28: 116-141.
CrossRef - Ilic, M.D., X. Le and J.Y. Joo, 2011. Efficient coordination of wind power and price-responsive demand. IEEE Trans. Power Syst., 26: 1875-1884.
CrossRef - Lei, W. and M. Shahidehpour, 2010. A hybrid model for day-ahead price forecasting. IEEE Trans. Power Syst., 25: 1519-1530.
CrossRef - Lira, F., C. Munoz, F. Nunez and A. Cipriano, 2009. Short-term forecasting of electricity prices in the colombian electricity market. IET Generation, Transmission Distribution, 3: 980-986.
CrossRef - Motamedi, A., H. Zareipour and W.D. Rosehart, 2012. Electricity price and demand forecasting in smart grids. IEEE Trans. Smart Grid, 3: 664-674.
CrossRef - Pindoriya, N.M., S.N. Singh and S.K. Singh, 2008. An adaptive wavelet neural network-based energy price forecasting in electricity markets. IEEE Trans. Power Syst., 23: 1423-1432.
CrossRefDirect Link - Rotering, N. and M. Ilic, 2011. Optimal charge control of plug-in hybrid electric vehicles in deregulated electricity markets. IEEE Trans. Power Syst., 26: 1021-1029.
CrossRef - Zhou, H., Q.Y. Zhu and P. Zhou, 2011. A hybrid price forecasting based on linear backfilling and sliding window algorithm. Int. Rev. Comput. Software, 6: 1131-1134.
Direct Link - Zhu, Q.Y., Y.Y. Yan, J. Ding and Y. Zhang, 2010. The commodities price extracting for shop online. Proceedings of the International Conference on Future Information Technology and Management Engineering (FITME), Volume: 2, October 9-10, 2010, Changzhou, China, pp: 317-320.
CrossRef - Zhu, Q.Y., S.Q. Cao, J. Ding and Z.Y. Han, 2011. Research on the price forecast without complete data based on web mining. Proceedings of the 10th International Symposium on Distributed Computing and Applications to Business, Engineering and Science, October 14-17, 2011, Karlsruhe, Germany, pp: 120-123.
- Zhu, Q.Y., S.Q. Cao, P. Zhou, Y.Y. Yan, H. Zhou, 2011. Integrated price forecast based on dichotomy backfilling and disturbance factor algorithm. Int. Rev. Comput. Software, 6: 1089-1093.
Direct Link - Zhu, Q.Y., P. Zhou, S.Q. Cao, Y.Y. Yan, J. Qian, 2012. A novel RDB-SW approach for commodities price dynamic trend analysis based on Web Mining. J. Digital Inform. Manage., 10: 230-235.
Direct Link - Zhu, Q.Y., J. Ding, Y.H. Yin and P. Zhou, 2012. A hybrid approach for new products discovery of cell phone based on web mining. J. Inform. Comput. Sci., 9: 5039-5046.
Direct Link