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Sales Prediction of Cardiac Products by Time Series and Deep Learning

Author

Listed:
  • Muhammad Waqas Arshad

    (Dept of Creative Technologies, Air University, Islamabad, Pakistan)

  • Syed Fahad Tahir

    (Dept of Computer Sciences, Air University, Islamabad, Pakistan)

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.

Suggested Citation

  • Muhammad Waqas Arshad & Syed Fahad Tahir, 2022. "Sales Prediction of Cardiac Products by Time Series and Deep Learning," International Journal of Innovations in Science & Technology, 50sea, vol. 4(5), pages 1-11, June.
    • Handle: RePEc:abq:ijist1:v:4:y:2022:i:5:p:1-11
    DOI: https://doi.org/10.33411/IJIST/2022040501
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    References listed on IDEAS

    as
    1. Neda Khalil Zadeh & Mohammad Mehdi Sepehri & Hamid Farvaresh, 2014. "Intelligent Sales Prediction for Pharmaceutical Distribution Companies: A Data Mining Based Approach," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-15, May.
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      More about this item

      Keywords

      Cardiac Products; Balloons; Stents; Time Series; Deep Learning; Decision Support;
      All these keywords.

      JEL classification:

      • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
      • Z0 - Other Special Topics - - General

      Statistics

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