Zhu Quanyin
Faculty of Computer Engineering, Huaiyin Institute of Technology, Huaian 223005, China
Pan Lu
Faculty of Computer Engineering, Huaiyin Institute of Technology, Huaian 223005, China
Yin Yonghua
Li Xiang
Faculty of Computer Engineering, Huaiyin Institute of Technology, Huaian 223005, China
ABSTRACT
In order to obtain suitable model and increase accuracy in price forecasting, data pretreatment approach via data normalization and the order of magnitude normalization which has the effect on price forecasting is proposed in this paper. The new data preprocessing method that normalized or normalizing the order of magnitude of the original data based on the proposed approaches are described in detail. The data of agricultural products extracted from Website based on improved Back Propagation (BP) neural network and Support Vector Machine (SVM) are utilized the proposed normalization methods and obtain the different results. Experiments demonstrate that the best forecasting average accuracy based on SVM is improved 0.33 percent by normalization and 0.35 percent by normalization of magnitude 10 and the best forecasting average accuracy based on improved BP neural network with no normalization is the best one, but the best of normalization of magnitude 100 cab be lifted 0.66 percent compare with the average accuracy of no normalization. Experiments demonstrate that the proposed approach performance and proves a new data pretreatment method via normalization is meaningful and useful for the model research of price forecasting.
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How to cite this article
Zhu Quanyin, Pan Lu, Yin Yonghua and Li Xiang, 2013. Influence on Normalization and Magnitude Normalization for Price Forecasting
of Agricultural Products. Information Technology Journal, 12: 3046-3057.
DOI: 10.3923/itj.2013.3046.3057
URL: https://scialert.net/abstract/?doi=itj.2013.3046.3057
DOI: 10.3923/itj.2013.3046.3057
URL: https://scialert.net/abstract/?doi=itj.2013.3046.3057
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 - Mohseni, S.A. and A.H. Tan, 2012. Optimization of neural networks using variable structure systems. IEEE Trans. Syst. Man Cybernetics Part B: Cybernetics, 42: 1645-1653.
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 - Yedidia, J.S., Y. Wang and S.C. Draper, 2011. Divide and concur and difference-map BP decoders for LDPC codes. IEEE Trans. Inform. Theory, 57: 786-802.
CrossRef - Zhang, L.M., N. Liu and P.Y. Yu, 2012. A novel instantaneous frequency algorithm and its application in stock index movement prediction. IEEE J. Selected Topics Signal Process., 6: 311-318.
CrossRef - 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.
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