{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:08:17Z","timestamp":1760242097314,"version":"build-2065373602"},"reference-count":82,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2018,12,18]],"date-time":"2018-12-18T00:00:00Z","timestamp":1545091200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>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).<\/jats:p>","DOI":"10.3390\/sym10120768","type":"journal-article","created":{"date-parts":[[2018,12,18]],"date-time":"2018-12-18T05:47:45Z","timestamp":1545112065000},"page":"768","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Degree Approximation-Based Fuzzy Partitioning Algorithm and Applications in Wheat Production Prediction"],"prefix":"10.3390","volume":"10","author":[{"given":"Rachna","family":"Jain","sequence":"first","affiliation":[{"name":"Computer Science and Engineering, Bharati Vidyapeeth\u2019s College of Engineering, New Delhi 110012, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nikita","family":"Jain","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, Bharati Vidyapeeth\u2019s College of Engineering, New Delhi 110012, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shivani","family":"Kapania","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, Bharati Vidyapeeth\u2019s College of Engineering, New Delhi 110012, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Le Hoang","family":"Son","sequence":"additional","affiliation":[{"name":"Division of Data Science, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam"},{"name":"Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,12,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/0165-0114(93)90372-O","article-title":"Fuzzy time series and its models","volume":"54","author":"Song","year":"1993","journal-title":"Fuzzy Sets Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/0165-0114(93)90355-L","article-title":"Forecasting enrollments with fuzzy time series-Part, I","volume":"45","author":"Song","year":"1993","journal-title":"Fuzzy Sets Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/0165-0114(94)90067-1","article-title":"Forecasting enrollments with fuzzy time series-Part II","volume":"62","author":"Song","year":"1994","journal-title":"Fuzzy Sets Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1016\/0165-0114(94)00315-X","article-title":"A new fuzzy time-series model of fuzzy number observations","volume":"73","author":"Song","year":"1995","journal-title":"Fuzzy Sets Syst."},{"key":"ref_5","first-page":"53","article-title":"Crop Yield Prediction Using Time Series Models","volume":"15","author":"Choudhury","year":"2014","journal-title":"J. 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