{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T09:10:18Z","timestamp":1768986618523,"version":"3.49.0"},"reference-count":28,"publisher":"Emerald","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,6,3]]},"abstract":"<jats:sec>\n                  <jats:title>Purpose<\/jats:title>\n                  <jats:p>This study addresses two key challenges in interval grey number prediction extensions: information loss in transformed sequences and sensitivity to weight selection. The research proposes a novel modeling approach for short-term time series forecasting, particularly in environments with limited data availability.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Design\/methodology\/approach<\/jats:title>\n                  <jats:p>The proposed approach integrates boxplots and Program Evaluation and Review Technique (PERT) with grey modeling through three main steps: data fuzzification using boxplots to infer upper and lower sequences, model construction employing various forecasting models and prediction aggregation using PERT concepts.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Findings<\/jats:title>\n                  <jats:p>The proposed procedure was validated using four datasets: two from previous studies, one from a simulation case and one from the UC Irvine Machine Learning Repository. The experimental results demonstrate that the proposed approach achieves superior prediction accuracy compared to Grey Model and its extensions.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Practical implications<\/jats:title>\n                  <jats:p>This approach is particularly valuable for industries with limited data availability. It provides a more reliable method for decision-making in environments where traditional data-driven approaches may be insufficient due to small sample sizes or fragmented datasets.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Originality\/value<\/jats:title>\n                  <jats:p>The study introduces a novel combination of boxplots and PERT with grey modeling, offering an innovative solution to overcome the limitations of current interval grey number prediction methods. This approach provides a more robust framework for handling uncertain and non-uniform numerical distributions in short-term time series forecasting.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1108\/gs-11-2024-0135","type":"journal-article","created":{"date-parts":[[2025,5,16]],"date-time":"2025-05-16T18:53:08Z","timestamp":1747421588000},"page":"602-619","source":"Crossref","is-referenced-by-count":4,"title":["Interval forecasting with grey models: a novel learning procedure for improved decision-making"],"prefix":"10.1108","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6041-3689","authenticated-orcid":false,"given":"Chien-Chih","family":"Chen","sequence":"first","affiliation":[{"name":"National Chin-Yi University of Technology Department of Information Management, , Taichung,","place":["Taiwan"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0616-5506","authenticated-orcid":false,"given":"Chih 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