Sales Forecasting using RNN
Kalaiarasan T R1, Anandkumar V2, Ratheesh Kumar A M3
1Kalaiarasan T R, Department of Information Technology, Sri Krishna College of Engineering and Technology, Coimbatore, (Tamil Nadu),India.
2Dr Anandkumar V, Department of Information Technology, Sri Krishna College of Engineering and Technology, Coimbatore, (Tamil Nadu),India.
3Ratheesh kumar A M, Department of Information Technology, Sri Krishna College of Engineering and Technology, Coimbatore, (Tamil Nadu),India.
Manuscript received on 30 June 2019 | Revised Manuscript received on 06 July 2019 | Manuscript published on 30 July 2019 | PP: 2748-2751 | Volume-8 Issue-9, July 2019 | Retrieval Number: I8428078919/19©BEIESP | DOI: 10.35940/ijitee.I8428.078919
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In today’s world, big malls and marts are in need of advanced prediction of sales forecasting for the future demand of the products. This leads the manufacturer to produce sufficient product without excess production and to avoid such loss, we need to predict the future demand of a product using Recurrent Neural Network. Long Short Term Memory (LSTM) model deals with the most important past behaviors and understands whether or not those behaviors are important features in making future predictions. Thus, we can reduce the wastage of the product and an increase in profit. In addition, the sales team can communicate with the manufacturing unit in case of insufficient product. This leads to avoiding excess quantity preparation from the production unit. Sales prediction and forecasting is always a best practice for company development.
Keywords: Sales Forecasting; Recurrent Neural Network; Long Short Term Memory (LSTM) Model.
Scope of the Article: Networked-Driven Multicourse Chips