Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Khatija, Liya; Vinoth, Dalvin; Kavitha, R. Title A name given to the resource Counterfactual Demand Forecasting Using Multivariate LSTM Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Proceedings of the International Conference on Research in Computational Intelligence and Communication Networks, ICRCICN;Issue;2025;pp.217-221 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICRCICN68210.2025.11365012" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICRCICN68210.2025.11365012</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105035365805?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105035365805?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Khatija L., Christ (Deemed-to-be University), Bengaluru, India; Vinoth D., Christ (Deemed-to-be University), Bengaluru, India; Kavitha R., Christ (Deemed-to-be University), Bengaluru, India Description An account of the resource Demand forecasting is a key part of running operations efficiently in the fast-changing retail and online shopping industries. Regular methods that use statistics often have trouble handling the complex, changing, and time-based patterns found in actual sales data. This study introduces a new way to predict demand that uses multivariate Long Short-Term Memory (LSTM) models. The models take both the order of sales over time and other factors like prices and weather into account. Three model designs were tested: a simple straightforward model, a pure LSTM model, and a new hybrid LSTM model that mixes time-based data with steady economic factors. The combined hybrid model worked the best, by successfully balancing learning from sequences with keeping things stable. The study did experiments to see what would happen if weather conditions changed, like extreme heat, cold, storms, or dry spells and compared normal forecasts with these changed scenarios to see how demand would shift for products and overall sales. The results show that this new framework not only makes better predictions but also gives useful information on how weather events can affect store sales. By linking prediction with 'what if' analysis, this research moves demand forecasting from just predicting what will happen to helping make better decisions. 2025 IEEE. Subject The topic of the resource counterfactual analysis; Demand forecasting; multivariate LSTM; retail time series; weather-aware forecasting Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISSN: 28323645; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online