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

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 (da...

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Bibliographic Details
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
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Summary: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).
ISSN:2073-8994
2073-8994
DOI:10.3390/sym10120768