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

• Published in: Symmetry (Basel) 2018-12, Vol.10 (12), p.768
• Main Authors: Jain, Rachna, Jain, Nikita, Kapania, Shivani, Son, Le Hoang
• Format: Article
• Language: English
• Subjects: Accuracy; Agricultural production; Algorithms; Approximation; Complexity; Crop yield; Data analysis; Datasets; Decision making; Economic forecasting; Fuzzy logic; fuzzy regression; fuzzy rules; Fuzzy sets; Information systems; Mathematical analysis; Methods; Optimization techniques; Partitioning; Prediction models; Sales forecasting; Time series; Wheat; wheat production prediction
• ISSN: 2073-8994; 2073-8994
• DOI: 10.3390/sym10120768

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).