Picture fuzzy regression functions approach for financial time series based on ridge regression and genetic algorithm

数学 自适应神经模糊推理系统 模糊集 数据挖掘 模糊逻辑 隶属函数 模糊数 模糊分类 去模糊化 模糊集运算 学位(音乐) 推论 模糊控制系统 算法 人工智能 计算机科学 物理 声学
作者
Eren Baş,Ufuk Yolcu,Erol Egrioğlu
出处
期刊:Journal of Computational and Applied Mathematics [Elsevier]
卷期号:370: 112656-112656 被引量:20
标识
DOI:10.1016/j.cam.2019.112656
摘要

Recent years, fuzzy inference systems are efficient tools for solving forecasting problems. Fuzzy inference systems are based on fuzzy sets and use membership values besides original data so a data augmentation mechanism is employed in the fuzzy inference. Picture fuzzy sets provide additional information to original data via positive degree membership, negative degree membership, neutral degree membership and refusal degree membership apart from fuzzy sets. The data augmentation with this additional information will be provided to build a better inference system than fuzzy inference systems. In this study, picture fuzzy inference system is proposed for forecasting purpose by using ridge regression and genetic algorithm. Ridge regression method is used to obtain picture fuzzy functions and genetic algorithm is used to emerge different information coming from systems which are designed for positive degree membership, negative degree membership and neutral degree membership. In the proposed method, picture fuzzification is provided by picture fuzzy clustering. The proposed inference system is tested by various stock exchange data sets. The forecasting of the proposed method is compared with well-known forecasting methods. The obtained results are evaluated according to different error measures such as root of mean square error and mean of absolute percentage error.
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