Machine learning-assisted efficient demand forecasting using endogenous and exogenous indicators for the textile industry
Muhammad Yasir, Yasmeen Ansari, Khalid Latif, Haider Maqsood, Adnan Habib, Jihoon Moon and Seungmin Rho
| Year of publication: |
2024
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|---|---|
| Authors: | Yasir, Muhammad ; Ansari, Yasmeen ; Latif, Khalid ; Maqsood, Haider ; Habib, Adnan ; Moon, Jihoon ; Rho, Seungmin |
| Published in: |
International journal of logistics : research and applications. - London [u.a.] : Taylor & Francis, ISSN 1469-848X, ZDB-ID 2020197-7. - Vol. 27.2024, 12, p. 2867-2886
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| Subject: | Demand forecasting | endogenous and exogenous indicators | linear regression | long shor-term memory | machine learning | textile industries | Textilindustrie | Textile industry | Prognoseverfahren | Forecasting model | Künstliche Intelligenz | Artificial intelligence | Nachfrage | Demand |
| Type of publication: | Article |
|---|---|
| Type of publication (narrower categories): | Aufsatz in Zeitschrift ; Article in journal |
| Language: | English |
| Other identifiers: | 10.1080/13675567.2022.2100334 [DOI] |
| Source: | ECONIS - Online Catalogue of the ZBW |
Persistent link: https://www.econbiz.de/10015446329