Pharmaceutical Sales Data Prediction Using Time Series Forecasting

Authors

  • Ankita M. Dept. of Electronics & Telecommunication Engineering, BMS College of Engineering
  • Ananya Srinivas Dept. of Electronics & Telecommunication Engineering, BMS College of Engineering
  • Anurag Soni Dept. of Electronics & Telecommunication Engineering, BMS College of Engineering
  • Garima Prajapati Dept. of Electronics & Telecommunication Engineering, BMS College of Engineering
  • Manjunath P. S. Dept. of Electronics & Telecommunication Engineering, BMS College of Engineering

Keywords:

LSTM, ARIMA, Dashboard, Time-series forecasting

Abstract

The pharmaceutical world is often considered evergreen, holding timeless importance in today’s world. It plays a major role in developing modern drugs and driving healthcare advancements. The pharmaceutical industry has come a long way, it now has the most advanced technologies and some of the world's leading scientists continually addressing global health challenges.

This booming industry mainly relies on efficient resource allocation, streamlined inventory management, cost-effectiveness, data-driven decision making and competitive strategies for its success. This paper discusses various methodologies in handling sales time series data within the pharmaceutical industry, aiming to enable data-informed decision making for industry professionals.

The objective aims at facilitating recommendation of sales and production of drugs by understanding trends and seasonality behind the data and accurately predicting its sales. The forecasting dashboard discussed later on in the paper, enables visualizing the sales and performance of a certain drug over a specified period of time. The research provides a simplified way to track and interpret drugs’ sales data for better decision making.

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References

Y. Han, “A forecasting method of pharmaceutical sales based on ARIMA-LSTM model,” 2020 5th International Conference on Information Science, Computer Technology and Transportation (ISCT).

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Khalil Zadeh, Neda & Sepehri, Mohammad Mehdi & Farvaresh, Hamid. (2014). “ Intelligent Sales Prediction for Pharmaceutical Distribution Companies: A Data Mining Based Approach.”

F. Majidi, M. Openja, F. Khomh and H. Li, “An Empirical Study on the Usage of Automated Machine Learning Tools,” in 2022 IEEE International Conference on Software Maintenance and Evolution (ICSME)

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Dariusz Kobielaa , Dawid Kreftaa , Weronika Krol , Paweł Weichbrothb, “ARIMA vs LSTM on NASDAQ stock exchange data” in 26th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES 2022)

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Published

29.01.2024

How to Cite

M., A. ., Srinivas, A. ., Soni, A. ., Prajapati , G. ., & P. S., M. . (2024). Pharmaceutical Sales Data Prediction Using Time Series Forecasting. International Journal of Intelligent Systems and Applications in Engineering, 12(13s), 681–696. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4649

Issue

Vol. 12 No. 13s (2024)

Section

Research Article

License

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