{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T20:01:42Z","timestamp":1775246502927,"version":"3.50.1"},"reference-count":36,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2024]]},"DOI":"10.1109\/access.2024.3516697","type":"journal-article","created":{"date-parts":[[2024,12,12]],"date-time":"2024-12-12T19:18:58Z","timestamp":1734031138000},"page":"190079-190091","source":"Crossref","is-referenced-by-count":22,"title":["Enhancing Time Series Product Demand Forecasting With Hybrid Attention-Based Deep Learning Models"],"prefix":"10.1109","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-8025-6308","authenticated-orcid":false,"given":"Xuguang","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Business, Computing and Social Sciences, University of Gloucestershire, Cheltenham, U.K."}]},{"given":"Pan","family":"Li","sequence":"additional","affiliation":[{"name":"Business School, University of Hull, Hull, U.K."}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-0261-0782","authenticated-orcid":false,"given":"Xu","family":"Han","sequence":"additional","affiliation":[{"name":"School of Business, Renmin University of China, Beijing, China"}]},{"given":"Yongbin","family":"Yang","sequence":"additional","affiliation":[{"name":"Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA"}]},{"given":"Yiwen","family":"Cui","sequence":"additional","affiliation":[{"name":"McCallum Graduate School of Business, Bentley University, Waltham, MA, USA"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1057\/palgrave.jors.2602597"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2015.11.010"},{"key":"ref3","volume-title":"Forecasting: Principles and Practice","author":"Hyndman","year":"2018"},{"key":"ref4","volume-title":"Time Series Analysis: Forecasting and Control","author":"Box","year":"2015"},{"issue":"3","key":"ref5","first-page":"1173","article-title":"A comparison of time series models for forecasting monthly tourism demand for Australia","volume":"35","author":"De Oliveira","year":"2019","journal-title":"Int. J. Forecasting"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2020.06.008"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/d16-1053"},{"key":"ref10","article-title":"Neural machine translation by jointly learning to align and translate","author":"Bahdanau","year":"2014","journal-title":"arXiv:1409.0473"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref12","first-page":"1","article-title":"Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Li"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/366"},{"key":"ref14","article-title":"Modeling long- and short-term temporal patterns with deep neural networks","author":"Lai","year":"2017","journal-title":"arXiv:1703.07015"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/DSAA.2018.00018"},{"issue":"4","key":"ref16","first-page":"1470","article-title":"Forecasting with deep learning: A review","volume":"36","author":"Bandara","year":"2020","journal-title":"Int. J. Forecasting"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-71918-2"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/S0925-2312(03)00372-2"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.3390\/w11050910"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1080\/00031305.2017.1380080"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0194889"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1201\/9780429321252"},{"key":"ref23","first-page":"1","article-title":"Sequence to sequence learning with neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"27","author":"Sutskever"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1406.1078"},{"key":"ref25","article-title":"An empirical evaluation of generic convolutional and recurrent networks for sequence modeling","author":"Bai","year":"2018","journal-title":"arXiv:1803.01271"},{"key":"ref26","first-page":"125","article-title":"WaveNet: A generative model for raw audio","volume-title":"Proc. 9th ISCA Speech Synth. Workshop","author":"Oord"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/476"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2019.03.017"},{"key":"ref29","first-page":"3504","article-title":"RETAIN: An interpretable predictive model for healthcare using reverse time attention mechanism","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Choi"},{"key":"ref30","first-page":"3319","article-title":"Axiomatic attribution for deep networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Sundararajan"},{"key":"ref31","first-page":"4765","article-title":"A unified approach to interpreting model predictions","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Lundberg"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1503.02531"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"ref34","first-page":"3146","article-title":"LightGBM: A highly efficient gradient boosting decision tree","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Ke"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2019.07.001"},{"key":"ref36","first-page":"1","article-title":"N-BEATS: Neural basis expansion analysis for interpretable time series forecasting","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Oreshkin"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/10380310\/10795122.pdf?arnumber=10795122","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,20]],"date-time":"2024-12-20T06:11:25Z","timestamp":1734675085000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10795122\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":36,"URL":"https:\/\/doi.org\/10.1109\/access.2024.3516697","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]}}}