Mixed order single variable intuitionistic fuzzy time series forecasting method based on a new artificial neural network and grey wolf optimization algorithm

Mixed order single variable intuitionistic fuzzy time series forecasting method based on a new artificial neural network and grey wolf optimization algorithm | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Mixed order single variable intuitionistic fuzzy time series forecasting method based on a new artificial neural network and grey wolf optimization algorithm Turan Cansu, Eren Bas, Erol Egrioglu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7846918/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted 14 You are reading this latest preprint version Abstract The ease of use of fuzzy time series and its success in forecasting performance has led to a rapid increase in this field. Although fuzzy time series methods work with membership values, intuitionistic fuzzy time series methods work based on both membership values and non-membership values. This study proposes a new mixed-order single variable intuitionistic fuzzy time series method for forecasting. The proposed method is based on a artificial neural network, intuitionistic fuzzy c-means algorithm and grey wolf optimization algorithm. The intuitionistic fuzzy time series is defined by using crisp values, memberships and non-memberships values. The fuzzy relations are determined based on a new artificial neural network based on the dendritic neuron model and grey wolf optimization algorithm. Forecast models will be created in two different ways based on membership and non-membership values, and the final forecasts will be obtained as a result of combining these models with the weights obtained by the grey wolf algorithm. The performance of the proposed method is compared with selected fuzzy methods in the literature by using different real-world time series. Physical sciences/Engineering Physical sciences/Mathematics and computing Intuitionistic fuzzy time series Artificial neural networks Dendritic neuron model artificial neural network Grey wolf optimization algorithm Forecasting Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 02 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 21 Dec, 2025 Reviews received at journal 18 Dec, 2025 Reviews received at journal 12 Dec, 2025 Reviews received at journal 08 Dec, 2025 Reviewers agreed at journal 27 Nov, 2025 Reviewers agreed at journal 26 Nov, 2025 Reviewers agreed at journal 25 Nov, 2025 Reviewers agreed at journal 25 Nov, 2025 Reviewers agreed at journal 25 Nov, 2025 Reviewers invited by journal 25 Nov, 2025 Editor invited by journal 16 Oct, 2025 Editor assigned by journal 15 Oct, 2025 Submission checks completed at journal 15 Oct, 2025 First submitted to journal 13 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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