Abstract
Time series model need lots of historical data, but the historical data are always incomplete, inaccurate and ambiguous. This uncertainty will affect the accuracy of forecasting. So the application of traditional forecasting model is limited. In this article, we apply the famous psychology law (Weber-Fischna law) in fuzzy time series models and predispose the historical data, so that we can fuzz data better. On the other hand, we use K-means cluster analysis to determine the center of class, which make the study more scientific and rigorous. In aspect of determining order, we find the best order by using the third criterion for judgment of order, which makes error less, makes precision of forecasting higher.
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References
Pan, H.: Time Series Analysis. The Press of University of International Business Economics (January 2006)
Hu, B.: Fuzzy theory basis. Wuhan University Press, Wuhan (2004)
Yu, X., Ren, X.: Multivariate Statistical Analysis. China Statistics Press, Beijing (1999)
Yang, Z.: Experimental psychology. Zhe Jiang Education Publishing House, Zhejiang (1998)
Lin, H.: Forecasting research of stock market based on fuzzy time series. Master Dissertation of Beijing University of Technology, pp. 1–56
Dan, H., Zhang, Y., Zhang, S.: A Kind of Modified K-mean Clustering Algorithm. Journal of Chongqing Technology and Business University: Natural Science Edition 26(2), 144–147 (2009)
Shen, B., Yao, M., Yi, W.-S.: Fuzzy time series analysis method based on least squares support vector machines. Journal of Zhejiang University (Engineering Science) 39(8), 1142–1147
Lin, Y., Yang, Y.: New Discussion of Forecasting Theory in stock market based on fuzzy time series. Statistics and Decision (8), 34–37 (2010)
Song, Q., Chissom, B.S.: Fuzzy time series and its models College of Education. Fuzzy Sets and Systems 54, 269–277 (1993)
Jilani, T.A., Muhammad, S., Burney, A.: A refined fuzzy time series model for stock market forecasting. Science Direct, 2857–2862
Shiva Raj Singh Department of mathematics, Bnaras Hindu University, Varaasi, A Simple time variant method for fuzzy time series forecasting India Taylor & Francis, pp. 305–321
Hao-Tien, Liu, N.-C., Wei, C.-G.: Improved time-variant fuzzy time series forecast Fuzzy Optim Decis Making, p. 4. Springer Science+Business Media, LLC (2009)
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Li, L., Gao, Y., Wang, Y. (2012). Fuzzy Time Series Forecasting Based on Weber-Fischna Law. In: Liu, C., Wang, L., Yang, A. (eds) Information Computing and Applications. ICICA 2012. Communications in Computer and Information Science, vol 307. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34038-3_45
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DOI: https://doi.org/10.1007/978-3-642-34038-3_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34037-6
Online ISBN: 978-3-642-34038-3
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