Degree Approximation-Based Fuzzy Partitioning Algorithm and Applications in Wheat Production Prediction

Symmetry

Shivani Kapania¹  Nikita Jain¹  Rachna Jain¹  LeHoang Son² 

[1] Computer Science and Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi 110012, India;Division of Data Science, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam;

关键词: wheat production prediction;    fuzzy rules;    time series;    fuzzy regression;   

DOI  :  10.3390/sym10120768

来源: DOAJ

【 摘 要 】

Recently, prediction modelling has become important in data analysis. In this paper, we propose a novel algorithm to analyze the past dataset of crop yields and predict future yields using regression-based approximation of time series fuzzy data. A framework-based algorithm, which we named DAbFP (data algorithm for degree approximation-based fuzzy partitioning), is proposed to forecast wheat yield production with fuzzy time series data. Specifically, time series data were fuzzified by the simple maximum-based generalized mean function. Different cases for prediction values were evaluated based on two-set interval-based partitioning to get accurate results. The novelty of the method lies in its ability to approximate a fuzzy relation for forecasting that provides lesser complexity and higher accuracy in linear, cubic, and quadratic order than the existing methods. A lesser complexity as compared to dynamic data approximation makes it easier to find the suitable de-fuzzification process and obtain accurate predicted values. The proposed algorithm is compared with the latest existing frameworks in terms of mean square error (MSE) and average forecasting error rate (AFER).

【 授权许可】

Unknown