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Forecast in the Pharmaceutical Area – Statistic Models vs Deep Learning

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Trends and Advances in Information Systems and Technologies (WorldCIST'18 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 747))

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Abstract

The main goal of this work was to evaluate the application of statistical and connectionist models for the problem of pharmacy sales forecasting. Since R is one of the most used software environment for statistical computation, we used the functions presented in its forecast package. These functions allowed for the construction of models that were then compared with the models developed using Deep Learning algorithms. The Deep Learning architecture was constructed using Long Short-Term Memory layers. It is very common to use statistical models in time series forecasting, namely the ARIMA model, however, with the arising of Deep Learning models our challenge was to compare the performance of these two approaches applied to pharmacy sales. The experiments studied, showed that for the used dataset, even a quickly developed LSTM model, outperformed the long used R forecasting package ARIMA model. This model will allow the optimization of stock levels, consequently the reduction of stock costs, possibly increase the sales and the optimization of human resources in a pharmacy.

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References

  1. Yousefi, N., Alibabaei, A.: Information flow in the pharmaceutical supply chain. Iran. J. Pharm. Res. 14(4), 1299–1303 (2015)

    Google Scholar 

  2. Microsoft: Business Intelligence for Healthcare : The new prescription for boosting Cost Management, Productivity and Medical Outcomes. Business Intelligence for Healthcare: The New Prescription for Boosting Cost Management, Productivity and Medical Outcomes. BusinessWe, February 2009

    Google Scholar 

  3. Ashrafi, N., Kelleher, L., Kuilboer, J.-P.: The impact of business intelligence on healthcare delivery in the USA. Interdiscip. J. Inf. 9(9), 117–130 (2014)

    Google Scholar 

  4. Hyndman, R.J., Athanasopoulos, G.: Forecasting: Principles and Practice, pp. 46–51. OTexts, Australia (2014)

    Google Scholar 

  5. Shumway, R.H., Stoffer, D.S.: Time Series Analysis and Its Applications, 3rd edn. Springer, Heidelberg (2017)

    Book  MATH  Google Scholar 

  6. Ohri, A.: Why every business analyst needs to learn R? (2012). http://analyticstraining.com/2012/why-every-business-analyst-needs-to-learn-r/. Accessed 01 June 2017

  7. Verma, E.: A Quick Guide to R Programming Language for Business Analytics (2015). https://www.simplilearn.com/r-programming-language-business-analytics-quick-guide-article. Accessed 01 June 2017

  8. Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015)

    Article  Google Scholar 

  9. Deng, L., Yu, D., Deep Learning : Methods and Applications (2013)

    Google Scholar 

  10. Russakovsky, O.: Convolutional Neural Networks for Visual Recognition (2015)

    Google Scholar 

  11. Olah, C.: Understanding LSTM Networks (2015)

    Google Scholar 

  12. Alahi, K., Goel, V., Ramanathan, A., Robicquet, Fei-Fei, L., Savarese, S.: Social LSTM : Human Trajectory Prediction in Crowded Spaces (2014)

    Google Scholar 

  13. Zaytar, M.A., El Amrani, C.: Sequence to sequence weather forecasting with long short-term memory recurrent neural networks. Int. J. Comput. Appl. 143(11), 975–8887 (2016)

    Google Scholar 

  14. May, M.: An Overview of Python Deep Learning Frameworks (2017)

    Google Scholar 

  15. Community, S.: SciPy Reference Guide (2015)

    Google Scholar 

  16. TensorFlow: https://www.tensorflow.org/. Accessed 01 Nov 2017

  17. Keras: https://keras.io/. Accessed 01 Nov 2017

  18. Brownlee, J.: Deep Learning With Python. https://machinelearningmastery.com/deep-learning-with-python/. Accessed 18 June 2017

  19. Steinberg, D.: Why Data Scientists Split Data into Train and Test (2014). http://info.salford-systems.com/blog/bid/337783/Why-Data-Scientists-Split-Data-into-Train-and-Test. Accessed 11 June 2017

  20. Hyndman, R.J.: New in forecast 4.0 (2012). https://robjhyndman.com/hyndsight/forecast4/. Accessed 12 June 2017

  21. Brownlee, J.: How to Normalize and Standardize Time Series Data in Python (2016). http://machinelearningmastery.com/normalize-standardize-time-series-data-python/. Accessed 18 June 2017

  22. Dieterle, D.F.: Overfitting, Underfitting and Model Complexity (2016)

    Google Scholar 

  23. Wesner, J.: MAE and RMSE — Which Metric is Better? (2016). https://medium.com/human-in-a-machine-world/mae-and-rmse-which-metric-is-better-e60ac3bde13d. Accessed 15 June 2017

  24. Mukaka, M.M.: Statistics corner: a guide to appropriate use of correlation coefficient in medical research. Malawi Med. J. 24(3), 69–71 (2012)

    Google Scholar 

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Acknowledgments

This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.

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Correspondence to Victor Alves .

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Ferreira, R., Braga, M., Alves, V. (2018). Forecast in the Pharmaceutical Area – Statistic Models vs Deep Learning. In: Rocha, Á., Adeli, H., Reis, L., Costanzo, S. (eds) Trends and Advances in Information Systems and Technologies. WorldCIST'18 2018. Advances in Intelligent Systems and Computing, vol 747. Springer, Cham. https://doi.org/10.1007/978-3-319-77700-9_17

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  • DOI: https://doi.org/10.1007/978-3-319-77700-9_17

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