{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T07:29:08Z","timestamp":1782372548687,"version":"3.54.5"},"reference-count":21,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,10,15]],"date-time":"2022-10-15T00:00:00Z","timestamp":1665792000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Industry and Information Technology of China for Cruise Program","award":["2018-473"],"award-info":[{"award-number":["2018-473"]}]},{"name":"Ministry of Industry and Information Technology of China for Cruise Program","award":["2019YFB1704403"],"award-info":[{"award-number":["2019YFB1704403"]}]},{"name":"National Key Research and Development Program of China","award":["2018-473"],"award-info":[{"award-number":["2018-473"]}]},{"name":"National Key Research and Development Program of China","award":["2019YFB1704403"],"award-info":[{"award-number":["2019YFB1704403"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Accurate sales forecasting can provide a scientific basis for the management decisions of enterprises. We proposed the xDeepFM-LSTM combined forecasting model for the characteristics of sales data of apparel retail enterprises. We first used the Extreme Deep Factorization Machine (xDeepFM) model to explore the correlation between the sales influencing features as much as possible, and then modeled the sales prediction. Next, we used the Long Short-Term Memory (LSTM) model for residual correction to improve the accuracy of the prediction model. We then designed and implemented comparison experiments between the combined xDeepFM-LSTM forecasting model and other forecasting models. The experimental results show that the forecasting performance of xDeepFM-LSTM is significantly better than other forecasting models. Compared with the xDeepFM forecasting model, the combined forecasting model has a higher optimization rate, which provides a scientific basis for apparel companies to make adjustments to adjust their demand plans.<\/jats:p>","DOI":"10.3390\/info13100497","type":"journal-article","created":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T02:29:46Z","timestamp":1665973786000},"page":"497","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Research on Apparel Retail Sales Forecasting Based on xDeepFM-LSTM Combined Forecasting Model"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3259-0688","authenticated-orcid":false,"given":"Tian","family":"Luo","sequence":"first","affiliation":[{"name":"School of Economics & Management, Shanghai Maritime University, Shanghai 201306, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daofang","family":"Chang","sequence":"additional","affiliation":[{"name":"Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5321-6943","authenticated-orcid":false,"given":"Zhenyu","family":"Xu","sequence":"additional","affiliation":[{"name":"Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"796","DOI":"10.1109\/TSC.2016.2599878","article-title":"Big Data Driven Mobile Traffic Understanding and Forecasting: A Time Series Approach","volume":"9","author":"Xu","year":"2016","journal-title":"IEEE Trans. Serv. Comput."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Yang, Y., and Lu, J.G. (2022). A Fusion Transformer for Multivariable Time Series Forecasting: The Mooney Viscosity Prediction Case. Entropy, 24.","DOI":"10.3390\/e24040528"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1007\/s10479-018-2847-6","article-title":"Relaxed support vector regression","volume":"276","author":"Panagopoulos","year":"2019","journal-title":"Ann. Oper. Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.ins.2021.12.097","article-title":"QueryNet: Querying neural networks for lightweight specialized models","volume":"589","author":"Jin","year":"2022","journal-title":"Inf. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1007\/s11277-016-3319-4","article-title":"Flexible Forecasting Model Based on Neural Networks","volume":"94","author":"Lee","year":"2017","journal-title":"Wirel. Pers. Commun."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"202858","DOI":"10.1109\/ACCESS.2020.3036421","article-title":"Optimization and Decomposition Methods in Network Traffic Prediction Model: A Review and Discussion","volume":"8","author":"Shi","year":"2020","journal-title":"IEEE Access"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2513","DOI":"10.1007\/s11280-020-00791-1","article-title":"Mode decomposition based deep learning model for multi-section traffic prediction","volume":"23","author":"Pholsena","year":"2020","journal-title":"World Wide Web"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1135","DOI":"10.1016\/j.ins.2020.09.067","article-title":"Fuzzy factorization machine","volume":"546","author":"Zhou","year":"2021","journal-title":"Inf. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2199","DOI":"10.1007\/s11036-021-01775-9","article-title":"Movie Recommendation System for Educational Purposes Based on Field-Aware Factorization Machine","volume":"26","author":"Lang","year":"2021","journal-title":"Mob. Netw. Appl."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"bbac079","DOI":"10.1093\/bib\/bbac079","article-title":"MLRDFM: A multi-view Laplacian regularized DeepFM model for predicting miRNA-disease associations","volume":"23","author":"Ding","year":"2022","journal-title":"Brief. Bioinform."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"75032","DOI":"10.1109\/ACCESS.2019.2921026","article-title":"Field-Aware Neural Factorization Machine for Click-Through Rate Prediction","volume":"7","author":"Zhang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1287\/mnsc.1040.0308","article-title":"An ARIMA supply chain model","volume":"51","author":"Gilbert","year":"2005","journal-title":"Manag. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"596","DOI":"10.1108\/GS-06-2020-0081","article-title":"Forecasting air quality in China using novel self-adaptive seasonal grey forecasting models","volume":"11","author":"Zhu","year":"2021","journal-title":"Grey Syst. Theory Appl."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1320","DOI":"10.1016\/j.energy.2018.10.032","article-title":"A novel hybridization of nonlinear grey model and linear ARIMA residual correction for forecasting US shale oil production","volume":"165","author":"Wang","year":"2018","journal-title":"Energy"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1775","DOI":"10.1002\/mrm.29317","article-title":"EPI phase error correction with deep learning (PEC-DL) at 7 T","volume":"88","author":"Wang","year":"2022","journal-title":"Magn. Reson. Med."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1865","DOI":"10.1109\/TSTE.2022.3156437","article-title":"ynamic-Error-Compensation-Assisted Deep Learning Framework for Solar Power Forecasting","volume":"13","author":"Su","year":"2022","journal-title":"IEEE Trans. Sustain. Energy"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.asoc.2017.09.040","article-title":"Deep neural network in QSAR studies using deep belief network","volume":"62","author":"Ghasemi","year":"2018","journal-title":"Appl. Soft Comput."},{"key":"ref_18","first-page":"1","article-title":"DexDeepFM: Ensemble Diversity Enhanced Extreme Deep Factorization Machine Model","volume":"16","author":"Chen","year":"2022","journal-title":"ACM Trans. Knowl. Discov. Data"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1145\/3233770","article-title":"Product-Based Neural Networks for User Response Prediction over Multi-Field Categorical Data","volume":"37","author":"Qu","year":"2019","journal-title":"ACM Trans. Inf. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"12107","DOI":"10.1007\/s00500-021-05861-8","article-title":"Route planning method for cross-border e-commerce logistics of agricultural products based on recurrent neural network","volume":"25","author":"Teng","year":"2021","journal-title":"Soft Comput."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Santra, A.S., and Lin, J.L. (2019). Integrating Long Short-Term Memory and Genetic Algorithm for Short-Term Load Forecasting. 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