{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T14:01:43Z","timestamp":1762956103176,"version":"build-2065373602"},"reference-count":55,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2019,4,1]],"date-time":"2019-04-01T00:00:00Z","timestamp":1554076800000},"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>This research introduces a neutrosophic forecasting approach based on neutrosophic time series (NTS). Historical data can be transformed into neutrosophic time series data to determine their truth, indeterminacy and falsity functions. The basis for the neutrosophication process is the score and accuracy functions of historical data. In addition, neutrosophic logical relationship groups (NLRGs) are determined and a deneutrosophication method for NTS is presented. The objective of this research is to suggest an idea of first-and high-order NTS. By comparing our approach with other approaches, we conclude that the suggested approach of forecasting gets better results compared to the other existing approaches of fuzzy, intuitionistic fuzzy, and neutrosophic time series.<\/jats:p>","DOI":"10.3390\/sym11040457","type":"journal-article","created":{"date-parts":[[2019,4,2]],"date-time":"2019-04-02T03:21:26Z","timestamp":1554175286000},"page":"457","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["A Refined Approach for Forecasting Based on Neutrosophic Time Series"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5572-0721","authenticated-orcid":false,"given":"Mohamed","family":"Abdel-Basset","sequence":"first","affiliation":[{"name":"Department of Operations Research, Faculty of Computers and Informatics, Zagazig University, Sharqiyah 44519, Egypt"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8012-5852","authenticated-orcid":false,"given":"Victor","family":"Chang","sequence":"additional","affiliation":[{"name":"School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK"}]},{"given":"Mai","family":"Mohamed","sequence":"additional","affiliation":[{"name":"Department of Operations Research, Faculty of Computers and Informatics, Zagazig University, Sharqiyah 44519, Egypt"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5560-5926","authenticated-orcid":false,"given":"Florentin","family":"Smarandache","sequence":"additional","affiliation":[{"name":"Mathematics Department, University of New Mexico, Gallup, NM 87301, USA"}]}],"member":"1968","published-online":{"date-parts":[[2019,4,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/0165-0114(93)90355-L","article-title":"Forecasting enrollments with fuzzy time series\u2014Part I","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(94)90067-1","article-title":"Forecasting enrollments with fuzzy time series\u2014Part II","volume":"62","author":"Song","year":"1994","journal-title":"Fuzzy Sets Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/0165-0114(95)00220-0","article-title":"Forecasting enrollments based on fuzzy time series","volume":"81","author":"Chen","year":"1996","journal-title":"Fuzzy Sets Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1080\/00207160410001712288","article-title":"Fuzzy forecasting based on fuzzy time series","volume":"81","author":"Lee","year":"2004","journal-title":"Int. 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