{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T10:08:40Z","timestamp":1769854120242,"version":"3.49.0"},"reference-count":26,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2020,11,28]],"date-time":"2020-11-28T00:00:00Z","timestamp":1606521600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2018YFC0116800"],"award-info":[{"award-number":["2018YFC0116800"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61772110"],"award-info":[{"award-number":["61772110"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"CERNET Innovation Project","award":["NGII20170711"],"award-info":[{"award-number":["NGII20170711"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Accurately forecasting sales is a significant challenge faced by almost all companies. In particular, most products have short lifecycles without the accumulation of historical sales data. Existing methods either fail to capture the context-specific, irregular trends or to integrate as much information as is available in the face of a data scarcity problem. To address these challenges, we propose a new model, called F-TADA, i.e., future-aware TADA, which is derived from trend alignment with dual-attention multi-task recurrent neural networks (TADA). We utilize two real-world supply chain sales data sets to verify our algorithm\u2019s performance and effectiveness on both long and short lifecycles. The experimental results show that the accuracy of the F-TADA is better than the original model. Our model\u2019s performance could be further improved, however, by appropriately increasing the length of the windows in the decoding stage. Finally, we develop a sales data prediction and analysis decision-making system, which can offer intelligent sales guidance to enterprises.<\/jats:p>","DOI":"10.3390\/info11120558","type":"journal-article","created":{"date-parts":[[2020,11,29]],"date-time":"2020-11-29T21:00:57Z","timestamp":1606683657000},"page":"558","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Future-Aware Trend Alignment for Sales Predictions"],"prefix":"10.3390","volume":"11","author":[{"given":"Yiwei","family":"Liu","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China"},{"name":"School of Innovation and Entrepreneurship, Dalian University of Technology, Dalian 116024, China"}]},{"given":"Lin","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China"},{"name":"School of Innovation and Entrepreneurship, Dalian University of Technology, Dalian 116024, China"}]},{"given":"Bo","family":"Jin","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China"},{"name":"School of Innovation and Entrepreneurship, Dalian University of Technology, Dalian 116024, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,28]]},"reference":[{"key":"ref_1","first-page":"267","article-title":"On a Method of Investigating Periodicities in Disturbed Series, with Special Reference to Wolfer\u2019s Sunspot Numbers Author","volume":"226","author":"Society","year":"2017","journal-title":"Stat. 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