{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T16:27:27Z","timestamp":1774024047020,"version":"3.50.1"},"reference-count":37,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,7,6]],"date-time":"2021-07-06T00:00:00Z","timestamp":1625529600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,7,6]],"date-time":"2021-07-06T00:00:00Z","timestamp":1625529600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,7,6]],"date-time":"2021-07-06T00:00:00Z","timestamp":1625529600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,7,6]]},"DOI":"10.1109\/icccnt51525.2021.9580139","type":"proceedings-article","created":{"date-parts":[[2021,11,3]],"date-time":"2021-11-03T19:29:48Z","timestamp":1635967788000},"page":"1-7","source":"Crossref","is-referenced-by-count":24,"title":["Demand Forecasting : Literature Review On Various Methodologies"],"prefix":"10.1109","author":[{"given":"Chaitanya","family":"Ingle","sequence":"first","affiliation":[]},{"given":"Dev","family":"Bakliwal","sequence":"additional","affiliation":[]},{"given":"Jayesh","family":"Jain","sequence":"additional","affiliation":[]},{"given":"Preeyesh","family":"Singh","sequence":"additional","affiliation":[]},{"given":"Preeti","family":"Kale","sequence":"additional","affiliation":[]},{"given":"Vaibhav","family":"Chhajed","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref33","first-page":"1","author":"li","year":"2019","journal-title":"MAD-GAN Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks"},{"key":"ref32","author":"husein","year":"2019","journal-title":"Generative Adversarial Networks Time Series Models to Forecast Medicine Daily Sales in Hospital"},{"key":"ref31","author":"kilimci","year":"2019","journal-title":"An Improved Demand Forecasting Model using Deep Learning Approach and Proposed Decision Integration Strategy for Supply Chain"},{"key":"ref30","author":"lakshmanan","year":"2020","journal-title":"Sales Demand Forecasting Using LSTM Network"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0219889"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/S0925-2312(01)00702-0"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-73499-4_39"},{"key":"ref34","first-page":"1","author":"li","year":"2018","journal-title":"Ano maly Detection with Generative Adversarial Networks for Multivariate Time Series"},{"key":"ref10","article-title":"The Elements of Statistical Learning Data Mining, Inference, and Prediction","author":"hastie","year":"2008","journal-title":"Springer series in statistics"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijhm.2013.04.003"},{"key":"ref12","article-title":"Predicting censored count data with CoM-Poisson regression, Working Paper","author":"sellers","year":"2010","journal-title":"Indian School of Business"},{"key":"ref13","article-title":"Food Demand Prediction using machine learning","volume":"7","author":"aishwarya","year":"2020","journal-title":"International Research Journal of Engineering and Technology (IRJET)"},{"key":"ref14","author":"maa","year":"2016","journal-title":"Support Demand forecasting with high dimensional data The case of SKU retail sales forecasting with intra- and inter-category promotional information"},{"key":"ref15","author":"huang","year":"2014","journal-title":"The value of competitive information in forecasting FMCG retail product sales and the variable selection problem"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2018.12.010"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/MCI.2009.932254"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"ref19","article-title":"An Advanced sales forecasting using Machine Learning Algorithm","volume":"5","author":"sri sai ramya","year":"2020","journal-title":"International Journal of Innovative Science and Research Technology"},{"key":"ref28","author":"makridakis","year":"1998","journal-title":"Forecasting Methods and Applications"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-24302-9_5"},{"key":"ref27","author":"xiong","year":"2015","journal-title":"Deep Learning Stock Volatility with Google Domestic Trends"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2017.01.022"},{"key":"ref6","volume":"4","author":"whittle","year":"1951","journal-title":"Hypothesis Testing in Time Series Analysis"},{"key":"ref29","author":"haykin","year":"2009","journal-title":"Neural Networks and Learning Machines"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1108\/JM2-11-2018-0192"},{"key":"ref8","first-page":"32","article-title":"Restaurant revenue management applying yield management to the restaurant industry","volume":"q39","author":"kimes","year":"1998","journal-title":"Cornell Hospitality Research"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ICBDA.2019.8713196"},{"key":"ref2","article-title":"Forecasting of demand using ARIMA model","volume":"1","author":"ghosh","year":"2020","journal-title":"American Journal of Computational and Applied Mathematics"},{"key":"ref9","author":"da veiga","year":"2014","journal-title":"Demand forecasting in food retail a comparison between the Holt-Winters and ARIMA models"},{"key":"ref1","author":"lasek","year":"2016","journal-title":"Restaurant Sales and Customer Demand Forecasting Literature Survey and Categorization of Methods"},{"key":"ref20","article-title":"Multivariate Time Series Forecast in Industrial Process Based on XGBoost and GRU","author":"zhai","year":"0","journal-title":"IEEE 9th Joint International Information Technology and Artificial Intelligence Conference (ITAIC 2020)"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-15-4015-8_37"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-15035-8_108"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ICIEECT.2017.7916583"},{"key":"ref23","year":"2015","journal-title":"Understanding LSTM Networks"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2788578"},{"key":"ref25","article-title":"Deep learning with long short-term memory networks for financial market predictions","author":"fischera","year":"2017","journal-title":"FAU Discussion Papers in Economics"}],"event":{"name":"2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT)","location":"Kharagpur, India","start":{"date-parts":[[2021,7,6]]},"end":{"date-parts":[[2021,7,8]]}},"container-title":["2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9579467\/9579470\/09580139.pdf?arnumber=9580139","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T16:55:58Z","timestamp":1652201758000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9580139\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,6]]},"references-count":37,"URL":"https:\/\/doi.org\/10.1109\/icccnt51525.2021.9580139","relation":{},"subject":[],"published":{"date-parts":[[2021,7,6]]}}}