{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T15:27:13Z","timestamp":1781882833782,"version":"3.54.5"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"1-2","license":[{"start":{"date-parts":[[2020,6,23]],"date-time":"2020-06-23T00:00:00Z","timestamp":1592870400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,6,23]],"date-time":"2020-06-23T00:00:00Z","timestamp":1592870400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Ann Oper Res"],"published-print":{"date-parts":[[2021,8]]},"DOI":"10.1007\/s10479-020-03666-w","type":"journal-article","created":{"date-parts":[[2020,6,23]],"date-time":"2020-06-23T17:02:45Z","timestamp":1592931765000},"page":"159-174","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":73,"title":["A data-driven forecasting approach for newly launched seasonal products by leveraging machine-learning approaches"],"prefix":"10.1007","volume":"303","author":[{"given":"Majd","family":"Kharfan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vicky Wing Kei","family":"Chan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4059-7134","authenticated-orcid":false,"given":"Tugba","family":"Firdolas Efendigil","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,6,23]]},"reference":[{"key":"3666_CR1","doi-asserted-by":"publisher","unstructured":"Au, K. F., Choi, T. M., & Yu, Y. (2008). Fashion retail forecasting by evolutionary neural networks. International Journal of Production Economics, 114(2), 615\u2013630. https:\/\/doi.org\/10.1016\/j.ijpe.2007.06.013. http:\/\/www.sciencedirect.com\/science\/article\/pii\/S0925527308000443. special Section on Logistics Management in Fashion Retail Supply Chains.","DOI":"10.1016\/j.ijpe.2007.06.013"},{"key":"3666_CR2","doi-asserted-by":"publisher","unstructured":"Brahmadeep, & Thomassey, S. (2016). Intelligent demand forecasting systems for fast fashion (pp. 145\u2013161). https:\/\/doi.org\/10.1016\/B978-0-08-100571-2.00008-7.","DOI":"10.1016\/B978-0-08-100571-2.00008-7"},{"issue":"1","key":"3666_CR3","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5\u201332. https:\/\/doi.org\/10.1023\/A:1010933404324.","journal-title":"Machine Learning"},{"issue":"3","key":"3666_CR4","doi-asserted-by":"publisher","first-page":"1140","DOI":"10.1016\/j.ejor.2006.12.004","volume":"184","author":"R Carbonneau","year":"2008","unstructured":"Carbonneau, R., Laframboise, K., & Vahidov, R. (2008). Application of machine learning techniques for supply chain demand forecasting. European Journal of Operational Research, 184(3), 1140\u20131154. https:\/\/doi.org\/10.1016\/j.ejor.2006.12.004.","journal-title":"European Journal of Operational Research"},{"issue":"4","key":"3666_CR5","first-page":"43","volume":"35","author":"CW Chase Jr","year":"2016","unstructured":"Chase, C. W, Jr. (2016). Machine learning is changing demand forecasting. Journal of Business Forecasting, 35(4), 43\u201345.","journal-title":"Journal of Business Forecasting"},{"key":"3666_CR6","doi-asserted-by":"publisher","unstructured":"Choi, T. M., Hui, C. L., & Yu, Y. (2011). Intelligent time series fast forecasting for fashion sales: A research agenda. In International conference on machine learning and cybernetics, ICMLC 2011, Guilin, China, July 10\u201313, 2011, Proceedings (pp. 1010\u20131014). https:\/\/doi.org\/10.1109\/ICMLC.2011.6016870.","DOI":"10.1109\/ICMLC.2011.6016870"},{"key":"3666_CR7","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1016\/j.dss.2013.10.008","volume":"59","author":"TM Choi","year":"2014","unstructured":"Choi, T. M., Hui, C. L., Liu, N., Ng, S. F., & Yu, Y. (2014). Fast fashion sales forecasting with limited data and time. Decision Support Systems, 59, 84\u201392. https:\/\/doi.org\/10.1016\/j.dss.2013.10.008.","journal-title":"Decision Support Systems"},{"issue":"4","key":"3666_CR8","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1007\/s00521-006-0077-3","volume":"16","author":"P Das","year":"2007","unstructured":"Das, P., & Chaudhury, S. (2007). Prediction of retail sales of footwear using feedforward and recurrent neural networks. Neural Computing and Applications, 16(4), 491\u2013502. https:\/\/doi.org\/10.1007\/s00521-006-0077-3.","journal-title":"Neural Computing and Applications"},{"key":"3666_CR9","first-page":"1157","volume":"3","author":"I Guyon","year":"2003","unstructured":"Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of Machine Learning Research, 3, 1157\u20131182.","journal-title":"Journal of Machine Learning Research"},{"key":"3666_CR10","doi-asserted-by":"publisher","unstructured":"Hui, P., & Choi, T. M. (2016). 5-Using artificial neural networks to improve decision making in apparel supply chain systems. In T.M. Choi (Ed.) Information systems for the fashion and apparel industry. Woodhead publishing series in textiles (pp. 97\u2013107). New York: Woodhead Publishing. https:\/\/doi.org\/10.1016\/B978-0-08-100571-2.00005-1. http:\/\/www.sciencedirect.com\/science\/article\/pii\/B9780081005712000051.","DOI":"10.1016\/B978-0-08-100571-2.00005-1"},{"key":"3666_CR11","volume-title":"An introduction to statistical learning: With applications in R","author":"G James","year":"2014","unstructured":"James, G., Witten, D., Hastie, T., & Tibshirani, R. (2014). An introduction to statistical learning: With applications in R. Berlin: Springer."},{"key":"3666_CR12","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1007\/978-3-642-39869-8_7","volume-title":"Fuzzy forecast combining for apparel demand forecasting","author":"M Kaya","year":"2014","unstructured":"Kaya, M., Ye\u015fil, E., Dodurka, M. F., & S\u0131rada\u011f, S. (2014). Fuzzy forecast combining for apparel demand forecasting (pp. 123\u2013146). Berlin: Springer. https:\/\/doi.org\/10.1007\/978-3-642-39869-8_7."},{"issue":"1","key":"3666_CR13","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1016\/j.ejor.2006.11.009","volume":"184","author":"K Kogan","year":"2008","unstructured":"Kogan, K., & Herbon, A. (2008). Production under periodic demand update prior to a single selling season: A decomposition approach. European Journal of Operational Research, 184(1), 133\u2013146. https:\/\/doi.org\/10.1016\/j.ejor.2006.11.009.","journal-title":"European Journal of Operational Research"},{"key":"3666_CR14","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.dss.2018.08.010","volume":"114","author":"A Loureiro","year":"2018","unstructured":"Loureiro, A., Migu\u00e9is, V., & da Silva, L. F. (2018). Exploring the use of deep neural networks for sales forecasting in fashion retail. Decision Support Systems, 114, 81\u201393. https:\/\/doi.org\/10.1016\/j.dss.2018.08.010.","journal-title":"Decision Support Systems"},{"key":"3666_CR15","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1016\/j.neucom.2013.08.012","volume":"128","author":"CJ Lu","year":"2014","unstructured":"Lu, C. J. (2014). Sales forecasting of computer products based on variable selection scheme and support vector regression. Neurocomputing, 128, 491\u2013499. https:\/\/doi.org\/10.1016\/j.neucom.2013.08.012.","journal-title":"Neurocomputing"},{"key":"3666_CR16","first-page":"2579","volume":"9","author":"L van der Maaten","year":"2008","unstructured":"van der Maaten, L., & Hinton, G. (2008). Visualizing data using t-SNE. Journal of Machine Learning Research, 9, 2579\u20132605.","journal-title":"Journal of Machine Learning Research"},{"issue":"1","key":"3666_CR17","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1016\/j.ejor.2010.11.001","volume":"211","author":"J Mostard","year":"2011","unstructured":"Mostard, J., Teunter, R., & de Koster, R. (2011). Forecasting demand for single-period products: A case study in the apparel industry. European Journal of Operational Research, 211(1), 139\u2013147. https:\/\/doi.org\/10.1016\/j.ejor.2010.11.001.","journal-title":"European Journal of Operational Research"},{"key":"3666_CR18","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1007\/s10288-016-0316-0","volume":"14","author":"GD Pillo","year":"2016","unstructured":"Pillo, G. D., Latorre, V., Lucidi, S., & Procacci, E. (2016). An application of support vector machines to sales forecasting under promotions. 4OR, 14, 309\u2013325.","journal-title":"4OR"},{"key":"3666_CR19","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/0377-0427(87)90125-7","volume":"20","author":"PJ Rousseeuw","year":"1987","unstructured":"Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53\u201365. https:\/\/doi.org\/10.1016\/0377-0427(87)90125-7.","journal-title":"Journal of Computational and Applied Mathematics"},{"issue":"1","key":"3666_CR20","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1016\/j.dss.2008.07.009","volume":"46","author":"ZL Sun","year":"2008","unstructured":"Sun, Z. L., Choi, T. M., Au, K. F., & Yu, Y. (2008). Sales forecasting using extreme learning machine with applications in fashion retailing. Decision Support Systems, 46(1), 411\u2013419. https:\/\/doi.org\/10.1016\/j.dss.2008.07.009.","journal-title":"Decision Support Systems"},{"issue":"2","key":"3666_CR21","doi-asserted-by":"publisher","first-page":"470","DOI":"10.1016\/j.ijpe.2010.07.018","volume":"128","author":"S Thomassey","year":"2010","unstructured":"Thomassey, S. (2010). Sales forecasts in clothing industry: The key success factor of the supply chain management. International Journal of Production Economics, 128(2), 470\u2013483. https:\/\/doi.org\/10.1016\/j.ijpe.2010.07.018.","journal-title":"International Journal of Production Economics"},{"issue":"1","key":"3666_CR22","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1016\/j.ejor.2002.09.001","volume":"161","author":"S Thomassey","year":"2005","unstructured":"Thomassey, S., Happiette, M., & Castelain, J. M. (2005). A short and mean-term automatic forecasting system-application to textile logistics. European Journal of Operational Research, 161(1), 275\u2013284. https:\/\/doi.org\/10.1016\/j.ejor.2002.09.001.","journal-title":"European Journal of Operational Research"},{"issue":"2","key":"3666_CR23","doi-asserted-by":"publisher","first-page":"614","DOI":"10.1016\/j.ijpe.2010.07.008","volume":"128","author":"W Wong","year":"2010","unstructured":"Wong, W., & Guo, Z. (2010). A hybrid intelligent model for medium-term sales forecasting in fashion retail supply chains using extreme learning machine and harmony search algorithm. International Journal of Production Economics, 128(2), 614\u2013624. https:\/\/doi.org\/10.1016\/j.ijpe.2010.07.008.","journal-title":"International Journal of Production Economics"},{"key":"3666_CR24","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1016\/j.knosys.2012.07.002","volume":"36","author":"M Xia","year":"2012","unstructured":"Xia, M., Zhang, Y., Weng, L., & Ye, X. (2012). Fashion retailing forecasting based on extreme learning machine with adaptive metrics of inputs. Knowledge-Based Systems, 36, 253\u2013259. https:\/\/doi.org\/10.1016\/j.knosys.2012.07.002.","journal-title":"Knowledge-Based Systems"},{"key":"3666_CR25","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1016\/S0925-2312(01)00702-0","volume":"50","author":"GP Zhang","year":"2003","unstructured":"Zhang, G. P. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159\u2013175.","journal-title":"Neurocomputing"}],"container-title":["Annals of Operations Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10479-020-03666-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10479-020-03666-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10479-020-03666-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,20]],"date-time":"2021-07-20T15:37:53Z","timestamp":1626795473000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10479-020-03666-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,23]]},"references-count":25,"journal-issue":{"issue":"1-2","published-print":{"date-parts":[[2021,8]]}},"alternative-id":["3666"],"URL":"https:\/\/doi.org\/10.1007\/s10479-020-03666-w","relation":{},"ISSN":["0254-5330","1572-9338"],"issn-type":[{"value":"0254-5330","type":"print"},{"value":"1572-9338","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,6,23]]},"assertion":[{"value":"23 June 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}