Abstract

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).

Highlights

  • Time series having indecision observations are called fuzzy time series, a term originally defined by Song and Chissom [1,2]

  • Time series include the recorded values of the variable in the past and include the present value. This method supports the discovery of arrangements and the inference of future events based on the patterns established as the chief focus material of time series analysis

  • Solutions to various practical problems related to finance, economics, marketing, and business as well as prediction in economic and sales forecasting, information systems forecasting, stock market prediction, the number of outpatient visits, etc., can be determined using time series

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Summary

Introduction

Time series having indecision observations are called fuzzy time series, a term originally defined by Song and Chissom [1,2]. In contrast to the above discussed methods, the proposed method in this study focused on diverse and finer levels of partitions with respect to fuzzy series data Using this degree approximation method based fuzzy portioning, a higher prediction accuracy was observed. The new method for forecasting wheat production with a fuzzy time series using degree approximation as a fuzzy relation for forecasting provided lesser complexity in the linear order Such simplicity was extended to cubic and quadratic polynomial approximation which minimized the time needed to generate relational equations based on complex min-max composition operations, as well as the various hits and trials of the defuzzification process that might be required to achieve better accuracy as used in [6,7,8,9,12] as well as by Singh [13].

Literature Review
Mathematical Preliminary
The Need of This Framework
DAbFP Algorithm
Numerical Example
Linear Polynomial
Discussion
Quadratic Polynomial
Cubic Polynomial
Results
Conclusions
Proposed Method DAbFP

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