一种启发式变阶直觉模糊时间序列预测模型

doi:10.11999/JEIT160013

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Abstract

Considering that the existing high-order models have limitations in forecast range and accuracy, a heuristic adaptive-order intuitionistic fuzzy time series forecasting model is built with the combination of the intuitionistic fuzzy sets theory. In this model, a direct fuzzy clustering algorithm is used to partition the universe of discourse into unequal intervals. The traditional method of ascertaining the membership and non-membership functions of intuitionistic fuzzy set are also modified to fit the intuitionistic fuzzy time series data. On these basis, variable high-order forecasting rules are established and the prior knowledge of tendency is used in defuzzification to extend the forecasting range. At last, contrast experiments on the enrollments of the University of Alabama and the daily average temperature of Beijing are carried out. The results show that the new model has a clear advantage of improving the forecast accuracy.

Key words： Intuitionistic fuzzy set Time series forecast Heuristic Adaptive order

Received: 04 January 2016 Published: 19 July 2016

PACS:

TP391

Fund:

The National Natural Science Foundation of China (61402517)

Corresponding Authors: WANG Yanan E-mail: wyn1988814@163.com

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	WANG Yanan
	LEI Yingjie
	WANG Yi
	ZHENG Kouquan

Cite this article:

WANG Yanan,LEI Yingjie,WANG Yi等. A Heuristic Adaptive-order Intuitionistic Fuzzy Time Series Forecasting Model[J]. JEIT, 2016, 38(11): 2795-2802.

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http://jeit.ie.ac.cn/EN/10.11999/JEIT160013 OR http://jeit.ie.ac.cn/EN/Y2016/V38/I11/2795

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