Abstract
Investment forecasting is an important issue for power engineering’s plan, investment and operation. In this paper, a new investment forecasting model is proposed based on covering rough set combined with neighborhood classifier theory. The classifier is used to classify the related historical data resulting in testing knowledge bases with certain testing precision. The knowledge base with the highest testing precision is chose to forecast the investment of new power engineering. The experiment results point out that the proposed model can forecast the power engineering investment effectually.
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References
Park, J.H., Park, Y.M., Lee, K.Y.: Composite modeling for adaptive short-term load forecasting. IEEE Trans. Power Syst. 6, 450–457 (1991)
Intriligator, M.D., Bodkin, R.G., Hsiao, C.: Econometric models, techniques, and applications. Prentice-Hall, American (1996)
Chen, A.S., Leung, M.T., Daouk, H.: Application of neural networks to an emerging financial market: forecasting and trading the Taiwan stock index. Computers and Operations Research 30, 901–923 (2003)
Enke, D., Thawornwong, S.: The use of data mining and neural networks for forecasting stock market returns. Expert Systems with Applications 29, 927–940 (2005)
Bonikowski, Z., Bryniarski, E., Wybraniex, U.: Extensions and intentions in the rough set theory. Information Sciences 107, 149–167 (1998)
Hu, Q.H., Yu, D.R., Xie, Z.X.: Neighborhood classifiers. Expert Systems with Applications 34, 866–876 (2008)
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© 2012 Springer-Verlag GmbH Berlin Heidelberg
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Zhu, L., Yan, Q., Li, C. (2012). Power Engineering Investment Forecasting Based on Covering Rough Set. In: Zhang, Y. (eds) Future Communication, Computing, Control and Management. Lecture Notes in Electrical Engineering, vol 141. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27311-7_31
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DOI: https://doi.org/10.1007/978-3-642-27311-7_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27310-0
Online ISBN: 978-3-642-27311-7
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