Forecasting Production Values using Fuzzy Logic Interval based Partitioning in Different Intervals

Abstract

Abstract-Fuzzy time series models have been put forward for rice production from many researchers around the globe, but the prediction has not been very accurate. Frequency density or ratio based partitioning methods have been used to represent the partition of discourse. We observed that various prediction models used 7 th interval based partitioning for their prediction models, so we wanted to find the reason for that and along with finding the explanation for that we have proposed a novel algorithm to make predictions easy. We have tried to provide an explanation for that. This paper has been put forth due to the motivation from previously published research works in prediction logics. In the current paper, we use a fuzzy time series model and provide a more accurate result than the methods already existent. To make such predictions, we have used interval based partitioning as the partition of discourse and actual production as the universe of discourse. Fuzzy models are used for prediction in many areas, like enrolments prediction, stock price analysis, weather forecasting, and rice production.

A Study of Time Series Forecasting Enrollments Using Fuzzy Interval Partitioning Method

“…Yolcu et al (2009) proposed the new length of intervals and determined the ratio for the intervals; the authors used a constrained optimization method for determining the intervals. Aggarwal et al (2017) used fuzzy logic interval partitioning method. They applied different length of intervals in enrollment data and determined the best length of interval to find the superior forecasting results.…”