Abstract
Purpose: The object of this research is to construct an optimal internal forecasting method in big data context.
Design/methodology/approach: An intelligent model construction, including consumer behavior and market information, structural changes detection, nonlinear pattern recognition, spatial causality, semantic processing mode is presented.
Findings: The major drawback in forecasting field is that the statistical forecasting result is derived from historical data but it often encounters non-realistic problem when people predict future trends or market changes in real world.
Practical Applications: Construction of Big Data platform will be a new technique provides to solve the structured change and uncertain problems. According to the artificial intelligence evolution and on line improvement to the market conditions, it will do a better performance to prevailing future event.
Originality: We efficiently integrate the idea of structure change, entropy and market behavior in the forecasting process.
Conclusion: Since historical time series analysis has difficult to prove the relationship/causality with future events. Especially in the case of a structural change, the future is full of high uncertainty, ambiguity and unexpected.
It is not the strongest nor the most intelligent who will survive, but those who can best manage change.
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References
Kim, D.-W., Lee, K.H., Lee, D.: On cluster validity index for estimation of the optimal number of fuzzy clusters. Pattern Recogn. 37(10), 2009–2025 (2004)
Kumar, K., Wu, B.: Detection of change points in time series analysis with fuzzy statistics. Int. J. Syst. Sci. 32(9), 1185–1192 (2001)
Lai, W., Wu, B.: Overcome the sort problem of low discrimination by interval fuzzy number. In: Innovative Management, Information and Production Nonlinear Mathematics for Uncertainty and its Applications, pp. 249–258. Springer, Berlin (2013)
Pakhira, M.K., Bandyopadhyay, S., Maulik, U.: A study of some fuzzy cluster validity indices, genetic clustering and application to pixel classification. Fuzzy Sets Syst. 15(2), 191–214 (2005)
Windham, M.P.: Cluster validity for fuzzy clustering algorithms. Fuzzy Sets Syst. 5(2), 177–185 (1981)
Namwong, J., Wattanakul, K.: Measuring of happiness of people in Tippanate’s community, Chiang Mai province. Empirical Econometrics Quant. Econ. Lett. 1(2), 59–70 (2012)
San, O.M., Huynh, V., Nakamori, Y.: An alternative extension of the K-means algorithm for clustering categorical data. Int. J. Appl. Math. Comput. Sci. 14(2), 241–247 (2004)
Sudtasan, T.: Detection of regime switching in stock prices before “window dressing” at the yearend using genetic algorithm. Int. J. Intell. Technol. Appl. Stat. 5(2), 143–155 (2012)
Sudtasan, T., Suriya, K.: Making profit in stock investment before XD dates by using genetic algorithm. In: Watada, J., et al (eds.) Innovative Management in Information and Production. Springer Science and Business Media, New York (2013)
Tseng, F., Tzeng, G.: A fuzzy seasonal ARIMA model for forecasting. Fuzzy Sets Syst. 126, 367–376 (2002)
Tseng, F., Tzeng, G., Yu, H., Yuan, B.: Fuzzy ARIMA model for forecasting the foreign exchange market. Fuzzy Sets Syst. 158, 609–624 (2005)
Wu, B., Chen, M.: Use fuzzy statistical methods in change periods detection. Appl. Math. Comput. 99, 241–254 (1999)
Wu, B., Nguyen, H.: New statistical analysis on the marketing research and efficiency evaluation with fuzzy data. Manag. Decis. 52, 1330–1342 (2014)
Yang, C.C., Cheng, Y.-T., Wu, B., Sriboonchitta, S.: Hold a mirror up to nature: A new approach on correlation evaluation with fuzzy data. In: Quantitative Modeling in Marketing and Management, pp. 285–299. World Scientific (2012)
Zeng, W., Li, H.: Relationship between similarity measure and entropy of interval valued fuzzy sets. Fuzzy Sets Syst. 157(11), 1477–1484 (2006)
Zhou, H.D.: Nonlinearity or structural break? - data mining in evolving financial data sets from a Bayesian model combination perspective. In: Proceedings of the 38th Hawaii International Conference on System Sciences (2005)
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Wu, B. (2018). Interval Forecasting on Big Data Context. In: Kreinovich, V., Sriboonchitta, S., Chakpitak, N. (eds) Predictive Econometrics and Big Data. TES 2018. Studies in Computational Intelligence, vol 753. Springer, Cham. https://doi.org/10.1007/978-3-319-70942-0_53
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DOI: https://doi.org/10.1007/978-3-319-70942-0_53
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