Multivariate Sales Forecasting Using Gated Recurrent Unit Network Model

<!DOCTYPE html>    <html>  <head> <meta charset="utf-8" /> <title>Multivariate Sales Forecasting Using Gated Recurrent Unit Network Model</title>  <link rel="shortcut icon" type="image/ico" href="./data/ijsom/coversheet/favicon.ico" />  <meta name="viewport" content="width=device-width, maximum-scale=1, initial-scale=1, user-scalable=0" />   <meta name="keywords" content="Keywords: Multivariate Sales Forecasting,Deep Learning,Recurrent Neural Networks (RNN),Supply chains,Gated Recurrent Unit (GRU)" /> <meta name="description" content="Market forecasting is an integral part of supply chain management. Machine learning models have turned a new page in predictive analysis and helped organizations achieve improved accuracy. This paper focuses on creating a Gated Recurrent Unit (GRU) model to predict sales for multiple stores as a multivariate time series. GRUs are a variation of Recurrent Neural Networks (RNNs) used to sequence modelling tasks. The dataset used to create the model contains the unit sales of 3,049 SKUs sold in 10 stores. The sales data from the 3049 SKUs were grouped into the 7 departments to use as input to the model. A Vector Autoregression (VAR) and LightGBM models were used to compare the GRU model predictions. Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) were used to compare the 2 models. The mean MAPE values for forecasts of the GRU, VAR, and LightGBM models were 13.77%, 14.87%, and 14.24% respectively, while MAE values were 68 Units, 72 Units, and 69 Units Respectively. The study reveals that the GRU model provides more accuracy for multivariate sales forecasting due to its ability to learn hidden patterns automatically and handle time mechanisms such as trends and seasonality." /> <meta name="title" content="Multivariate Sales Forecasting Using Gated Recurrent Unit Network Model" /> <meta name="googlebot" content="NOODP" /> <meta name="citation_title" content="Multivariate Sales Forecasting Using Gated Recurrent Unit Network Model" /> <meta name="citation_author" content="Jayasekara, W.A. Roshan S." /> <meta name="citation_author_institution" content="Department of Transport and Logistics Management, Faculty of Engineering, University of Moratuwa, Katubedda 10400, Sri Lanka." /> <meta name="citation_author" content="Sugathadasa, P. T. Ranil S." /> <meta name="citation_author_institution" content="Center for Supply Chain, Operations and Logistics Optimization, University of Moratuwa, Katubedda 10400, Sri Lanka" /> <meta name="citation_author" content="Herath, Oshadhi" /> <meta name="citation_author_institution" content="Department of Transport and Logistics Management, Faculty of Engineering, University of Moratuwa, Katubedda 10400, Sri Lanka." /> <meta name="citation_author" content="Perera, Niles" /> <meta name="citation_author_institution" content="Center for Supply Chain, Operations and Logistics Optimization, University of Moratuwa, Katubedda 10400, Sri Lanka" /> <meta name="citation_abstract" content="Market forecasting is an integral part of supply chain management. Machine learning models have turned a new page in predictive analysis and helped organizations achieve improved accuracy. This paper focuses on creating a Gated Recurrent Unit (GRU) model to predict sales for multiple stores as a multivariate time series. GRUs are a variation of Recurrent Neural Networks (RNNs) used to sequence modelling tasks. The dataset used to create the model contains the unit sales of 3,049 SKUs sold in 10 stores. The sales data from the 3049 SKUs were grouped into the 7 departments to use as input to the model. A Vector Autoregression (VAR) and LightGBM models were used to compare the GRU model predictions. Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) were used to compare the 2 models. The mean MAPE values for forecasts of the GRU, VAR, and LightGBM models were 13.77%, 14.87%, and 14.24% respectively, while MAE values were 68 Units, 72 Units, and 69 Units Respectively. The study reveals that the GRU model provides more accuracy for multivariate sales forecasting due to its ability to learn hidden patterns automatically and handle time mechanisms such as trends and seasonality." /> <meta name="citation_id" content="2944" /> <meta name="citation_publication_date" content="2024/11/01" /> <meta name="citation_date" content="2024-11-01" /> <meta name="citation_journal_title" content="International Journal of Supply and Operations Management" /> <meta name="citation_issn" content="23831359" /> <meta name="citation_volume" content="11" /> <meta name="citation_issue" content="4" /> <meta name="citation_firstpage" content="390" /> <meta name="citation_lastpage" content="416" /> <meta name="citation_publisher" content="Kharazmi University" /> <meta name="citation_doi" content="10.22034/ijsom.2024.109038.2141" /> <meta name="DC.Identifier" content="10.22034/ijsom.2024.109038.2141" /> <meta name="citation_abstract_html_url" content="http://www.ijsom.com/article_2944.html" /> <meta name="citation_pdf_url" content="http://www.ijsom.com/article_2944_832003315c4ab2e3180f011f686c4090.pdf" /> <meta name="DC.Title" content="Multivariate Sales Forecasting Using Gated Recurrent Unit Network Model" /> <meta name="DC.Source" content="International Journal of Supply and Operations Management" /> <meta name="DC.Date" content="01/11/2024" /> <meta name="DC.Date.issued" content="2024-11-01" /> <meta name="DC.Format" content="application/pdf" /> <meta name="DC.Contributor" content="Jayasekara, W.A. Roshan S." /> <meta name="DC.Contributor" content="Sugathadasa, P. T. Ranil S." /> <meta name="DC.Contributor" content="Herath, Oshadhi" /> <meta name="DC.Contributor" content="Perera, Niles" /> <meta name="og:title" content="Multivariate Sales Forecasting Using Gated Recurrent Unit Network Model" /> <meta name="og:description" content="Market forecasting is an integral part of supply chain management. Machine learning models have turned a new page in predictive analysis and helped organizations achieve improved accuracy. This paper focuses on creating a Gated Recurrent Unit (GRU) model to predict sales for multiple stores as a multivariate time series. GRUs are a variation of Recurrent Neural Networks (RNNs) used to sequence modelling tasks. The dataset used to create the model contains the unit sales of 3,049 SKUs sold in 10 stores. The sales data from the 3049 SKUs were grouped into the 7 departments to use as input to the model. A Vector Autoregression (VAR) and LightGBM models were used to compare the GRU model predictions. Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) were used to compare the 2 models. The mean MAPE values for forecasts of the GRU, VAR, and LightGBM models were 13.77%, 14.87%, and 14.24% respectively, while MAE values were 68 Units, 72 Units, and 69 Units Respectively. The study reveals that the GRU model provides more accuracy for multivariate sales forecasting due to its ability to learn hidden patterns automatically and handle time mechanisms such as trends and seasonality." /> <meta name="og:url" content="http://www.ijsom.com/article_2944.html" />  <link href="./themes/base/front/assets/css/social-icon-font.css" rel="stylesheet" type="text/css" />  <link href="./themes/base/front/assets/plugins/bootstrap/css/bootstrap.min.css?v=0.02" rel="stylesheet" type="text/css" /> <link href="./themes/old/front/assets/css/header.css?v=0.05" rel="stylesheet" type="text/css" /> <link href="./themes/old/front/assets/css/footer.css" rel="stylesheet" type="text/css" /> <link href="./inc/css/essentials.css?v=0.2" rel="stylesheet" type="text/css" /> <link href="./inc/css/cookieconsent.min.css" rel="stylesheet" type="text/css" /> <link href="./inc/css/print.css" rel="stylesheet" type="text/css" media="print"/>  <link href="./themes/base/front/assets/plugins/bootstrap/css/bootstrap-ltr.min.css" rel="stylesheet" type="text/css" /> <link href=" ./themes/base/front/assets/css/gfonts-OpenSans.css" rel="stylesheet" type="text/css" /> <link href="./themes/old/front/assets/css/accordian.css" rel="stylesheet" type="text/css" /> <link href="./themes/base/front/assets/css/academicons.min.css" rel="stylesheet" type="text/css" />  <meta name="google-site-verification" content="UlbWpwabckk_wTgRmzoJCVPEYKnomtCLcftujpXdou4" /> <link href="./data/ijsom/coversheet/stl_front.css?v=0.87" rel="stylesheet" type="text/css" /> <link href="./data/ijsom/coversheet/stl.css" rel="stylesheet" type="text/css" />  <link rel="alternate" type="application/rss+xml" title="RSS feed" href="ju.rss" /> <script type="text/javascript" src="./themes/base/front/assets/plugins/jquery/jquery.min.js?v=0.5"></script> <script type="text/javascript" src="./inc/js/common.js?v=0.1"></script> <script type="text/javascript" src="./inc/js/jquery/cookieconsent.min.js"></script>   <script src="inc/js/article.js?v=0.31" type="text/javascript" ></script> </head> <body class="ltr len"> <div class="container" id="header"> <div class="row"> <div class="col-xs-12 text-center"> <img src="./data/ijsom/coversheet/head_en.jpg" class="img-responsive text-center" style="display:-webkit-inline-box; 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Roshan S. Jayasekara</a> <sup><a href="#aff1" >1</a></sup> </li> <li class="padding-3"> <a href="./?_action=article&au=124990&_au=P.+T.+Ranil+S.++Sugathadasa">P. T. Ranil S. Sugathadasa</a> <sup><a href="#aff2" >2</a></sup> </li> <li class="padding-3"> <a href="./?_action=article&au=124991&_au=Oshadhi++Herath">Oshadhi Herath</a> <sup><a href="#aff1" >1</a></sup> </li> <li class="padding-3"> <a href="./?_action=article&au=121473&_au=Niles++Perera">Niles Perera</a> <sup><a href="mailto:hniles@uom.lk" data-toggle="tooltip" data-placement="bottom" title="Email to Corresponding Author"><i class="fa fa-envelope-o" ></i></a></sup> <sup class="ltr"><a class=" text-green" href="https://www.orcid.org/0000-0001-6329-5967" data-toggle="tooltip" data-placement="bottom" data-html="true" title="ORCID: 0000-0001-6329-5967" target="_blank"><i class="ai ai-orcid size-13" ></i></a></sup> <sup><a href="#aff2" >2</a></sup> </li> </ul> <p class="margin-bottom-3 ltr" id="aff1"> <sup>1</sup> Department of Transport and Logistics Management, Faculty of Engineering, University of Moratuwa, Katubedda 10400, Sri Lanka. </p> <p class="margin-bottom-3 ltr" id="aff2"> <sup>2</sup> Center for Supply Chain, Operations and Logistics Optimization, University of Moratuwa, Katubedda 10400, Sri Lanka </p> <div class="margin-bottom-3 ltr" id="ar_doi" title="DOI"><i class="ai ai-doi size-25 text-orange"></i> <span dir="ltr"><a href="https://dx.doi.org/10.22034/ijsom.2024.109038.2141">10.22034/ijsom.2024.109038.2141</a></span></div> <p style="margin:12px -2px 0 -2px"><strong>Abstract</strong></p> <div class="padding_abstract justify ltr">Market forecasting is an integral part of supply chain management. Machine learning models have turned a new page in predictive analysis and helped organizations achieve improved accuracy. This paper focuses on creating a Gated Recurrent Unit (GRU) model to predict sales for multiple stores as a multivariate time series. GRUs are a variation of Recurrent Neural Networks (RNNs) used to sequence modelling tasks. The dataset used to create the model contains the unit sales of 3,049 SKUs sold in 10 stores. The sales data from the 3049 SKUs were grouped into the 7 departments to use as input to the model. A Vector Autoregression (VAR) and LightGBM models were used to compare the GRU model predictions. Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) were used to compare the 2 models. The mean MAPE values for forecasts of the GRU, VAR, and LightGBM models were 13.77%, 14.87%, and 14.24% respectively, while MAE values were 68 Units, 72 Units, and 69 Units Respectively. The study reveals that the GRU model provides more accuracy for multivariate sales forecasting due to its ability to learn hidden patterns automatically and handle time mechanisms such as trends and seasonality.</div> <p class="padding-0" style="margin:12px -2px 0 -2px"><strong>Keywords</strong></p> <ul class="block list-inline list-inline-seprator margin-bottom-6 ltr"> <li class="padding-3"> <a class="tag_a" href="./?_action=article&kw=106545&_kw=Keywords%3A+Multivariate+Sales+Forecasting" >Keywords: Multivariate Sales Forecasting</a> </li> <li class="padding-3"> <a class="tag_a" href="./?_action=article&kw=106486&_kw=Deep+Learning" >Deep Learning</a> </li> <li class="padding-3"> <a class="tag_a" href="./?_action=article&kw=106546&_kw=Recurrent+Neural+Networks+%28RNN%29" >Recurrent Neural Networks (RNN)</a> </li> <li class="padding-3"> <a class="tag_a" href="./?_action=article&kw=105093&_kw=Supply+Chains" >Supply Chains</a> </li> <li class="padding-3"> <a class="tag_a" href="./?_action=article&kw=106547&_kw=Gated+Recurrent+Unit+%28GRU%29" >Gated Recurrent Unit (GRU)</a> </li> </ul> </div> <hr> <div class="page_break"></div> <div class="panel"> <div class="panel-heading card-header"> <h4 class="panel-title "> <a data-toggle="collapse" data-parent="#accordions" href="#collapsesRef"><i class="fa fa-plus"></i> References</a> </h4> </div> <div id="collapsesRef" class="panel-collapse collapse"> <div class="panel-body justify"> <div class="padding-3 margin-top-3 ltr justify">References </div> <div class="padding-3 margin-top-3 ltr justify">Azubuike, I., & Kosemoni, O. 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On the stationarity of multivariate time series for correlation-based data analysis. <em>Proceedings - IEEE International Conference on Data Mining, ICDM</em>, 805–808. https://doi.org/10.1109/ICDM.2005.109 </div> <div class="padding-3 margin-top-3 ltr justify">Zhang, G., Eddy Patuwo, B., & Y. Hu, M. (1998). Forecasting with artificial neural networks: The state of the art. <em>International Journal of Forecasting</em>, <em>14</em>(1), 35–62. https://doi.org/10.1016/S0169-2070(97)00044-7</div> </div> </div> </div> </div> </div>   <div class="col-lg-3 col-md-3 col-sm-4"> <div class="panel panel-default my_panel-default margin-bottom-10"> <div class="panel-body ar_info_pnl" id="ar_info_pnl_cover"> <div id="pnl_cover"> <div class="row" > <div class="col-xs-6 col-md-6 nomargin-bottom"> <a href="javascript:loadModal('International Journal of Supply and Operations Management', './data/ijsom/coversheet/cover_en.jpg')"> <img src="data/ijsom/coversheet/cover_en.jpg" alt="International Journal of Supply and Operations Management" style="width: 100%;"> </a> </div> <div class="col-xs-6 col-md-6 nomargin-bottom"> <h6><a href="./issue_542_546.html">Volume 11, Issue 4</a><br/>November 2024<div id="sp_ar_pages">Pages <span dir="ltr">390-416</span></div></h6> </div> </div> </div> </div> </div>  <div 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<ul class="list-group list-group-bordered list-group-noicon" style="display:block !important;max-height:9999px"> <li class="list-group-item"><a class="tag_a">Article View: <i>279</i></a></li> <li class="list-group-item"><a class="tag_a">PDF Download: <i>199</i></a></li> </ul> </div> </div> </div> </div>  </div> </section> <div id="cite-apa" class="modal fade" tabindex="-1" role="dialog" aria-labelledby="myModalLabel" aria-hidden="true"> <div class="modal-dialog"> <div class="modal-content">  <div class="modal-header"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> <h4 class="modal-title" id="myModalLabel">APA</h4> </div>  <div class="modal-body"> <p>Jayasekara, W., Sugathadasa, P., Herath, O., & Perera, N. (2024). Multivariate Sales Forecasting Using Gated Recurrent Unit Network Model. <em>International Journal of Supply and Operations Management</em>, 11(4), 390-416. doi: 10.22034/ijsom.2024.109038.2141</p> </div> </div> </div> </div> <div id="cite-mla" class="modal fade" tabindex="-1" role="dialog" aria-labelledby="myModalLabel" aria-hidden="true"> <div class="modal-dialog"> <div class="modal-content">  <div class="modal-header"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> <h4 class="modal-title" id="myModalLabel">MLA</h4> </div>  <div class="modal-body"> <p>W.A. Roshan S. Jayasekara; P. T. Ranil S. Sugathadasa; Oshadhi Herath; Niles Perera. "Multivariate Sales Forecasting Using Gated Recurrent Unit Network Model". <em>International Journal of Supply and Operations Management</em>, 11, 4, 2024, 390-416. doi: 10.22034/ijsom.2024.109038.2141</p> </div> </div> </div> </div> <div id="cite-harvard" class="modal fade" tabindex="-1" role="dialog" aria-labelledby="myModalLabel" aria-hidden="true"> <div class="modal-dialog"> <div class="modal-content">  <div class="modal-header"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> <h4 class="modal-title" id="myModalLabel">HARVARD</h4> </div>  <div class="modal-body"> <p>Jayasekara, W., Sugathadasa, P., Herath, O., Perera, N. (2024). 'Multivariate Sales Forecasting Using Gated Recurrent Unit Network Model', <em>International Journal of Supply and Operations Management</em>, 11(4), pp. 390-416. doi: 10.22034/ijsom.2024.109038.2141</p> </div> </div> </div> </div> <div id="cite-vancouver" class="modal fade" tabindex="-1" role="dialog" aria-labelledby="myModalLabel" aria-hidden="true"> <div class="modal-dialog"> <div class="modal-content">  <div class="modal-header"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> <h4 class="modal-title" id="myModalLabel">VANCOUVER</h4> </div>  <div class="modal-body"> <p>Jayasekara, W., Sugathadasa, P., Herath, O., Perera, N. Multivariate Sales Forecasting Using Gated Recurrent Unit Network Model. <em>International Journal of Supply and Operations Management</em>, 2024; 11(4): 390-416. doi: 10.22034/ijsom.2024.109038.2141</p> </div> </div> </div> </div> </div> </div>   <section class="alternate padding-xxs"> </section>   <div class="container"> <footer id="footer"> <div class="scrollup" id="scroll" href="#"><span></span></div> <div class="row"> <div class="col-md-2">  <h4 class="">Explore Journal</h4> <ul class="footer-links list-unstyled"> <li id="fli_home"><a href="./">Home</a></li> <li id="fli_about"><a href="./journal/about">About Journal</a></li> <li id="fli_Edb"><a href="./journal/editorial.board">Editorial Board</a></li> <li id="fli_submit"><a href="./author">Submit Manuscript</a></li> <li id="fli_contactus"><a href="./journal/contact.us">Contact Us</a></li> <li id="fli_glossary"><a href="./journal/glossary">Glossary</a></li> <li id="fli_order_hrdj"><a href="./journal/subscription.form">Hard Copy Subscription</a></li> <li id="fli_sitemap"><a href="./sitemap.xml?usr">Sitemap</a></li> </ul>  </div> <div class="col-md-3">  <h4 class="">Latest News</h4> <ul class="footer-posts list-unstyled"> <li> <a href="./news?newsCode=173">SD of ISC: Sustainable Development of Intelligent Supply Chains based on Trends and Future Directions: Application of Novel Solution Techniques</a> <small class="ltr">2023-03-05</small> </li> </ul>  </div> <div class="col-md-3">  <div><p><a title="Linkedin" href="http://www.linkedin.com/company/ijsom?trk=eml-cp_mktg-btn-welcome-20120607%2F"><img src="images/linkedin.jpg" alt="linkedin" /></a></p></div>  </div> <div class="col-md-4">  <h4 class="">Newsletter Subscription</h4> <p>Subscribe to the journal newsletter and receive the latest news and updates</p> <form class="validate" action="" method="post" data-success="Subscription saved successfully." data-toastr-position="bottom-right"> <input type="hidden" name="_token" value="0c30c9886f2696a1e94517aca9a0fae10f7c4a4b8ab1e598"/> <div class="input-group"> <span class="input-group-addon"><i class="fa fa-envelope"></i></span> <input type="email" id="email" name="email" required="required" class="form-control required sbs_email" placeholder="Enter your Email" oninvalid="this.setCustomValidity('Enter a valid email address.')" oninput="this.setCustomValidity('')"> <span class="input-group-btn"> <button class="btn btn-primary mybtn" type="submit">Subscribe</button> </span> </div> </form>   <div class="margin-top-20"> <a class="noborder" href="" target="_blank" class="social-icon social-icon-border social-facebook pull-left block" data-toggle="tooltip" data-placement="top" title="Facebook"> <i class="fa fa-facebook-square" aria-hidden="true"></i> </a> <a class="noborder" href="" target="_blank" class="social-icon social-icon-border social-facebook pull-left block" data-toggle="tooltip" data-placement="top" title="Twitter"> <i class="fa fa-twitter-square" aria-hidden="true"></i> </a> <a class="noborder" href="" target="_blank" class="social-icon social-icon-border social-facebook pull-left block" data-toggle="tooltip" data-placement="top" title="Linkedin"> <i class="fa fa-linkedin-square" aria-hidden="true"></i> </a> <a class="noborder" href="./ju.rss" class="social-icon social-icon-border social-rss pull-left block" data-toggle="tooltip" data-placement="top" title="Rss"><i class="fa fa-rss-square" aria-hidden="true"></i></a> </div> </div> </div> <div class="copyright" style="position: relative"> <ul class="nomargin list-inline mobile-block"> <li>© Journal Management System. <span id='sp_crt'>Powered by <a target='_blank' href='https://www.sinaweb.net/'>Sinaweb</a></span></li> </ul> </div> </footer> </div>  </div>   <a href="#" id="toTop_old"></a>  <div id="preloader"> <div class="inner"> <span class="loader"></span> </div> </div>  <script type="text/javascript">var plugin_path = './themes/base/front/assets/plugins/';</script> <script type="text/javascript" src="./themes/base/front/assets/js/scripts.js?v=0.02"></script>   <script type="text/javascript"> $('ul.nav li.dropdown').hover(function() { if (window.matchMedia('(max-width: 767px)').matches) return; 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Multivariate Sales Forecasting Using Gated Recurrent Unit Network Model