基于信息熵的概率加权模糊时间序列预测模型研究及应用
Research and Application of Probability Weighted Fuzzy Time Series Forecasting Model Based on Information Entropy
DOI: 10.12677/PM.2023.137213, PDF,   
作者: 朱思瑾：成都理工大学数理学院，四川 成都
关键词: 信息熵；模糊时间序列；隶属函数；Information Entropy； Fuzzy Time Series； Membership Function
摘要: 在模糊时间序列(FTS)预测模型中，论域的确定及其划分方法是影响预测精度的关键因素。为了能够充分考虑到数据提供的信息和数据的不均匀性等特征，本文使用最小熵原理方法(MEPA)来划分论域并建立隶属函数，然后在传统FTS预测模型的基础上结合概率加权方法，建立PWFTS模型，最后使用中国新增人口数据对模型进行训练，实验结果表明本文提出的结合信息熵的概率加权模糊时间序列模型能够有效预测时间序列的变化。
Abstract: In the fuzzy time series prediction model, the determination of the universe domain and its division method are the key factors affecting the prediction accuracy. In order to be able to fully take into account the information provided by the data and the characteristics such as the inhomogeneity of the data, this paper adopts the Minimum Entropy Principle Approach (MEPA) to divide the universe of discourse and establish the membership functions. Then, on the basis of the traditional FTS prediction model, the PWFTS model is established by combining the probability weighting method. Finally, the model is trained using the new population data of China. The experimental results show that the probability-weighted fuzzy time series model combined with information entropy proposed in this paper can effectively predict the changes of time series.
文章引用：朱思瑾. 基于信息熵的概率加权模糊时间序列预测模型研究及应用[J]. 理论数学, 2023, 13(7): 2069-2079. https://doi.org/10.12677/PM.2023.137213

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