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- Publisher Website: 10.1016/j.orl.2019.08.008
- Scopus: eid_2-s2.0-85072072800
- WOS: WOS:000500037400001
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Article: Quantile forecasting and data-driven inventory management under nonstationary demand
Title | Quantile forecasting and data-driven inventory management under nonstationary demand |
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Authors | |
Keywords | Data-driven decision making Quantile forecasting Neural networks Newsvendor model Nonstationary time series |
Issue Date | 2019 |
Citation | Operations Research Letters, 2019, v. 47, n. 6, p. 465-472 How to Cite? |
Abstract | © 2019 Elsevier B.V. In this paper, a single-step framework for predicting quantiles of time series is presented. Subsequently, we propose that this technique can be adopted as a data-driven approach to determine stock levels in the environment of newsvendor problem and its multi-period extension. Theoretical and empirical findings suggest that our method is effective at modeling both weakly stationary and some nonstationary time series. On both simulated and real-world datasets, the proposed approach outperforms existing statistical methods and yields good newsvendor solutions. |
Persistent Identifier | http://hdl.handle.net/10722/296202 |
ISSN | 2023 Impact Factor: 0.8 2023 SCImago Journal Rankings: 0.449 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Cao, Ying | - |
dc.contributor.author | Shen, Zuo Jun Max | - |
dc.date.accessioned | 2021-02-11T04:53:03Z | - |
dc.date.available | 2021-02-11T04:53:03Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | Operations Research Letters, 2019, v. 47, n. 6, p. 465-472 | - |
dc.identifier.issn | 0167-6377 | - |
dc.identifier.uri | http://hdl.handle.net/10722/296202 | - |
dc.description.abstract | © 2019 Elsevier B.V. In this paper, a single-step framework for predicting quantiles of time series is presented. Subsequently, we propose that this technique can be adopted as a data-driven approach to determine stock levels in the environment of newsvendor problem and its multi-period extension. Theoretical and empirical findings suggest that our method is effective at modeling both weakly stationary and some nonstationary time series. On both simulated and real-world datasets, the proposed approach outperforms existing statistical methods and yields good newsvendor solutions. | - |
dc.language | eng | - |
dc.relation.ispartof | Operations Research Letters | - |
dc.subject | Data-driven decision making | - |
dc.subject | Quantile forecasting | - |
dc.subject | Neural networks | - |
dc.subject | Newsvendor model | - |
dc.subject | Nonstationary time series | - |
dc.title | Quantile forecasting and data-driven inventory management under nonstationary demand | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1016/j.orl.2019.08.008 | - |
dc.identifier.scopus | eid_2-s2.0-85072072800 | - |
dc.identifier.volume | 47 | - |
dc.identifier.issue | 6 | - |
dc.identifier.spage | 465 | - |
dc.identifier.epage | 472 | - |
dc.identifier.isi | WOS:000500037400001 | - |
dc.identifier.issnl | 0167-6377 | - |