Sales Prediction of Cardiac Products by Time Series and Deep Learning

Muhammad Waqas Arshad () and Syed Fahad Tahir
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Muhammad Waqas Arshad: Dept of Creative Technologies, Air University, Islamabad, Pakistan
Syed Fahad Tahir: Dept of Computer Sciences, Air University, Islamabad, Pakistan

International Journal of Innovations in Science & Technology, 2022, vol. 4, issue 5, 1-11

Abstract: Maintaining inventory level to avoid high inventory costs is an issue for Cardiac Product Distribution Companies (CPDCs) because of the shortage of their products which affect their sale and causes loss of the customer. This research aims to provide a method for predicting the upcoming demand of the Balloon and Stents by using time series analysis (Auto Regression Integrated Moving Average) and Deep learning (Long-Short Term Memory). To conduct this research, data was collected from Pakistan’s leading cardiac product distributors to determine the method's performance. The findings were compared using Mean absolute error (MAE) and Root Mean Square Error (RMSE). Resulst conclude that the ARIMA algorithm successfully forecasts cardiac products sale.

Keywords: Cardiac Products; Balloons; Stents; Time Series; Deep Learning; Decision Support (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:abq:ijist1:v:4:y:2022:i:5:p:1-11

DOI: 10.33411/IJIST/2022040501

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