{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T19:22:58Z","timestamp":1743016978218,"version":"3.40.3"},"publisher-location":"Cham","reference-count":13,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031217524"},{"type":"electronic","value":"9783031217531"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-21753-1_45","type":"book-chapter","created":{"date-parts":[[2022,11,20]],"date-time":"2022-11-20T10:02:32Z","timestamp":1668938552000},"page":"465-473","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Sequence to\u00a0Sequence Long Short-Term Memory Network for\u00a0Footwear Sales Forecasting"],"prefix":"10.1007","author":[{"given":"Lu\u00eds","family":"Santos","sequence":"first","affiliation":[]},{"given":"Lu\u00eds Miguel","family":"Matos","sequence":"additional","affiliation":[]},{"given":"Lu\u00eds","family":"Ferreira","sequence":"additional","affiliation":[]},{"given":"Pedro","family":"Alves","sequence":"additional","affiliation":[]},{"given":"M\u00e1rio","family":"Viana","sequence":"additional","affiliation":[]},{"given":"Andr\u00e9","family":"Pilastri","sequence":"additional","affiliation":[]},{"given":"Paulo","family":"Cortez","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,21]]},"reference":[{"key":"45_CR1","volume-title":"Time Series Analysis: Forecasting and Control","author":"G Box","year":"1976","unstructured":"Box, G., Jenkins, G.: Time Series Analysis: Forecasting and Control. Holden Day, San Francisco (1976)"},{"key":"45_CR2","doi-asserted-by":"publisher","unstructured":"Chopra, S., Meindl, P.: Supply chain management. strategy, planning & operation. In: Boersch, C., Elschen, R. (eds.) Das Summa Summarum Des Management, pp. 265\u2013275. Springer, Cham (2007). https:\/\/doi.org\/10.1007\/978-3-8349-9320-5_22","DOI":"10.1007\/978-3-8349-9320-5_22"},{"key":"45_CR3","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1007\/978-3-319-47364-2_26","volume-title":"International Joint Conference SOCO\u201916-CISIS\u201916-ICEUTE\u201916","author":"P Cortez","year":"2017","unstructured":"Cortez, P., Matos, L.M., Pereira, P.J., Santos, N., Duque, D.: Forecasting store foot traffic using facial recognition, time series and support vector machines. In: Gra\u00f1a, M., L\u00f3pez-Guede, J.M., Etxaniz, O., Herrero, \u00c1., Quinti\u00e1n, H., Corchado, E. (eds.) SOCO\/CISIS\/ICEUTE -2016. AISC, vol. 527, pp. 267\u2013276. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-47364-2_26"},{"key":"45_CR4","doi-asserted-by":"crossref","unstructured":"Ensafi, Y., Amin, S.H., Zhang, G., Shah, B.: Time-series forecasting of seasonal items sales using machine learning-a comparative analysis. Int. J. Inf. Manag. Data Insights 2(1):100058 (2022)","DOI":"10.1016\/j.jjimei.2022.100058"},{"key":"45_CR5","doi-asserted-by":"crossref","unstructured":"Fernandes, C., et al.: A deep learning approach to prevent problematic movements of industrial workers based on inertial sensors. In International Joint Conference on Neural Networks, IJCNN 2022, Padua, Italy, 18\u201323 July 2022. IEEE (2022)","DOI":"10.1109\/IJCNN55064.2022.9892409"},{"key":"45_CR6","unstructured":"Hyndman, R.J., Athanasopoulos, G.: Forecasting: Principles and Practice, 3rd edn. O Texts (2021)"},{"key":"45_CR7","doi-asserted-by":"crossref","unstructured":"Makatjane, K., Moroke, N.: Comparative study of holt-winters triple exponential smoothing and seasonal arima: forecasting short term seasonal car sales in south africa. Risk Gov. Control Financ. Markets Institutions 6 (2016)","DOI":"10.22495\/rgcv6i1art8"},{"key":"45_CR8","doi-asserted-by":"crossref","unstructured":"Meng, J., Yang, X., Yang, C., Liu, Y.: Comparative analysis of prophet and LSTM model in drug sales forecasting. 1910 (2021). IOP Publishing","DOI":"10.1088\/1742-6596\/1910\/1\/012059"},{"key":"45_CR9","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1016\/j.eswa.2016.12.036","volume":"73","author":"N Oliveira","year":"2017","unstructured":"Oliveira, N., Cortez, P., Areal, N.: The impact of microblogging data for stock market prediction: Using twitter to predict returns, volatility, trading volume and survey sentiment indices. Expert Syst. Appl. 73, 125\u2013144 (2017)","journal-title":"Expert Syst. Appl."},{"key":"45_CR10","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1016\/j.rcim.2014.12.015","volume":"34","author":"P Ramos","year":"2015","unstructured":"Ramos, P., Santos, N., Rebelo, R.: Performance of state space and ARIMa models for consumer retail sales forecasting. Rob. Comput.-Integrat. Manuf. 34, 151\u2013163 (2015)","journal-title":"Rob. Comput.-Integrat. Manuf."},{"key":"45_CR11","doi-asserted-by":"crossref","unstructured":"Siami-Namini, S., Tavakoli, N., Namin, A.S.: A comparison of ARIMA and LSTM in forecasting time series. In: Arif Wani, M., Kantardzic, M.M., Mouchaweh, M.S., Gama, J., Lughofer, E. (eds.) 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018, Orlando, FL, USA, 17\u201320 December 2018, pp. 1394\u20131401. IEEE (2018)","DOI":"10.1109\/ICMLA.2018.00227"},{"issue":"4","key":"45_CR12","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1016\/S0169-2070(00)00065-0","volume":"16","author":"LJ Tashman","year":"2000","unstructured":"Tashman, L.J.: Out-of-sample tests of forecasting accuracy: an analysis and review. Int. Forecast. J. 16(4), 437\u2013450 (2000)","journal-title":"Int. Forecast. J."},{"key":"45_CR13","series-title":"Lecture Notes in Electrical Engineering","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1007\/978-981-10-5768-7_2","volume-title":"Advanced Manufacturing and Automation VII","author":"Q Yu","year":"2018","unstructured":"Yu, Q., Wang, K., Strandhagen, J.O., Wang, Y.: Application of long short-term memory neural network to sales forecasting in retail\u2014a case study. In: Wang, K., Wang, Y., Strandhagen, J.O., Yu, T. (eds.) IWAMA 2017. LNEE, vol. 451, pp. 11\u201317. Springer, Singapore (2018). https:\/\/doi.org\/10.1007\/978-981-10-5768-7_2"}],"container-title":["Lecture Notes in Computer Science","Intelligent Data Engineering and Automated Learning \u2013 IDEAL 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-21753-1_45","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T12:17:52Z","timestamp":1710332272000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-21753-1_45"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031217524","9783031217531"],"references-count":13,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-21753-1_45","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"21 November 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IDEAL","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Data Engineering and Automated Learning","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Manchester","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 November 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ideal2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ideal-conf.com\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"79","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"52","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"66% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.9","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.1","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}