Abstract
To solve problems such as imprecise domain partition and improvement of limitations in establishing fuzzy time series forecasting methods that make it hard to adapt to the emergence of new relationships, a new fuzzy time series forecasting algorithm is proposed. Based on fuzzy theory, the domain partition algorithm is optimized by establishing the connection between the minimum value of historical data and the value of parameter spec; then a second division is divided depending on different numbers in each domain; the third-order fuzzy logic is to establish the fuzzy rules to obtain different sets of trends; finally, the predicted value is obtained by defuzzification. The experiment shows that this method features high feasibility and adaptability.
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Acknowledgments
This paper was supported by the National Natural Science Foundation of China: “Research on Trusted Technologies for The Terminals in The Distributed Network Environment” (Grant 60903018), “Research on the Security Technologies for Cloud Computing Platform” (Grant 61272543), “National Twelfth Five-Year Key Technology Research and Development Program of the Ministry of Science and Technology of China” (Grant 2013BAB06B04), and “Key Technology Project of China Huaneng Group” (Grant HNKJ13-H17-04).
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Liu, C., Zhang, Y., Yang, F., Zhou, W., Lv, X. (2015). Fuzzy Time Series Forecasting Algorithm Based on Maximum Interval Value. In: Wong, W. (eds) Proceedings of the 4th International Conference on Computer Engineering and Networks. Lecture Notes in Electrical Engineering, vol 355. Springer, Cham. https://doi.org/10.1007/978-3-319-11104-9_88
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DOI: https://doi.org/10.1007/978-3-319-11104-9_88
Publisher Name: Springer, Cham
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Online ISBN: 978-3-319-11104-9
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