{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T15:08:02Z","timestamp":1768748882215,"version":"3.49.0"},"reference-count":40,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2020,9,6]],"date-time":"2020-09-06T00:00:00Z","timestamp":1599350400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Forecasting stock prices plays an important role in setting a trading strategy or determining the appropriate timing for buying or selling a stock. The use of technical analysis for financial forecasting has been successfully employed by many researchers. The existing qualitative based methods developed based on fuzzy reasoning techniques cannot describe the data comprehensively, which has greatly limited the objectivity of fuzzy time series in uncertain data forecasting. Extended fuzzy sets (e.g., fuzzy probabilistic set) study the fuzziness of the membership grade to a concept. The cloud model, based on probability measure space, automatically produces random membership grades of a concept through a cloud generator. In this paper, a cloud model-based approach was proposed to confirm accurate stock based on Japanese candlestick. By incorporating probability statistics and fuzzy set theories, the cloud model can aid the required transformation between the qualitative concepts and quantitative data. The degree of certainty associated with candlestick patterns can be calculated through repeated assessments by employing the normal cloud model. The hybrid weighting method comprising the fuzzy time series, and Heikin\u2013Ashi candlestick was employed for determining the weights of the indicators in the multi-criteria decision-making process. Fuzzy membership functions are constructed by the cloud model to deal effectively with uncertainty and vagueness of the stock historical data with the aim to predict the next open, high, low, and close prices for the stock. The experimental results prove the feasibility and high forecasting accuracy of the proposed model.<\/jats:p>","DOI":"10.3390\/e22090991","type":"journal-article","created":{"date-parts":[[2020,9,6]],"date-time":"2020-09-06T23:12:49Z","timestamp":1599433969000},"page":"991","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Application of Cloud Model in Qualitative Forecasting for Stock Market Trends"],"prefix":"10.3390","volume":"22","author":[{"given":"Oday A.","family":"Hassen","sequence":"first","affiliation":[{"name":"Ministry of Education, Wasit Education Directorate, Kut 52001, Iraq"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2723-1549","authenticated-orcid":false,"given":"Saad M.","family":"Darwish","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Institute of Graduate Studies and Research, Alexandria University, 163 Horreya Avenue, El\u2013Shatby, Alexandria 21526, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nur A.","family":"Abu","sequence":"additional","affiliation":[{"name":"Faculty of Information and Communication Technology, University Teknikal Malaysia Melaka, Melaka 76100, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zaheera Z.","family":"Abidin","sequence":"additional","affiliation":[{"name":"Faculty of Information and Communication Technology, University Teknikal Malaysia Melaka, Melaka 76100, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.knosys.2017.09.023","article-title":"Intraday prediction of Borsa Istanbul using convolutional neural networks and feature correlations","volume":"137","author":"Gunduz","year":"2017","journal-title":"Knowl. 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