Time series forecasting and application in pharmaceutical area
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Abstract
Forecasting is a prediction of an event that will happen in the future. This is very important for effective planning. The most popular quantitative forecasting based on historical data analysis is time series forecasting. Time series is a series of observations listed in the order of time. Time series forecasting is the process of predicting the future value of time series data based on past observations. Time series data consists of the following components: Trend, Seasonal, Cyclic and Noise. The standard procedures based on Cross Industry Standard Process for Data Mining (CRISP-DM) framework principles are often used to develop models in time series forecasting. The model development technique used to define the relationships between variables and to forecast time series data can be divided into statistical method and machine learning method. In the field of pharmacy, time series forecasting has been applied in two interesting areas, each with different purposes and techniques. The first is drug expenditure forecasting. Studies were conducted by using long-term retrospective data for developing models to predict drug expenditure in the next period. The results of studies are used to provide decision-making information for well prepare in any situation. The second application is to forecast the demand for drug products, that can cause a profitable in complex supply chain of pharmaceutical industry by using the model from drug consumption or drug sales data. Modeling techniques require more precise prediction to promptly determine the process in organization. In pharmaceutical domain, time series forecasting is one of the methods to fetch the consecutive drug information to analyze for finding the answer of some interest in advance. In addition, forecasting techniques be adjusted to better fit available data The efficiency in forecasting depends on choosing a method that is appropriate for the context of the time series data, study period, and forecasting objectives.
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References
Ahmed, N. K., Atiya, A. F., Gayar, N. E., & El-Shishiny, H. (2010). An Empirical Comparison of Machine Learning Models for Time Series Forecasting. Econometric Reviews, 29(5-6), 594-621. https://doi.org/https://doi.org/10.1080/07474938.2010.481556
Bengio, Y., Goodfellow, I., & Courville, A. (2015). Deep Learning.
Bontempi, G., Taieb, S., & Borgne, Y.-A. L. (2013). Machine Learning Strategies for Time Series Forecasting. ResearchGate, 62-77.
Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., & Wirth, R. (2000). CRISP-DM 1.0: Step-by-Step Data Mining Guide. SPSS Inc.
Choosri, P., Noimanee, J., & Pansin, K. (2019). AI for Government Administration and Services (C. Teerathitiyangkul, Ed. 1 ed.). Digital Government Development Agency.
Dix, P. (2020). Time series forecasting methods. InfluxData Inc. https://www.influxdata.com/time-series-forecasting-methods/#:~:text=Time%20series%20forecasting%20is%20a,Astronomy
Hyndman, R. J., & Athanasopoulos, G. (2018, 11 October 2022). Forecasting: principles and practice. OTexts. Retrieved 8 May from https://otexts.com/fpp2/index.html
Illukkumbura, A. (2021). Introduction to Time Series Analysis (1 ed.). Anusha Illukkumbura.
Ingle, C., bakliwal, D., Jain, J., singh, P., Kale, P., & Chhajed, V. (2021, 6-8 July). Demand Forecasting: Literature Review On Various Methodologies. 12th International Conference on Computing Communication and Networking Technologies (ICCCNT) India.
Kaladee, A., Laopaiboon, M., & Ungpansattawong, S. (2013). Assessment of Pharmacy Interventions Articles with Interrupted Time Series Design in Healthcare Journals: A Systematic Review. Srinagarind Med J, 28, 7.
Kaushik, S., Choudhury, A., Dasgupta, N., Natarajan, S., Pickett, L. A., & Dutt, V. (2017). Using LSTMs for Predicting Patient’s Expenditure on Medications. 2017 International Conference on Machine learning and Data Science,
Kaushik, S., Choudhury, A., Sheron, P. K., Dasgupta, N., Natarajan, S., Pickett, L. A., & Dutt, V. (2020). AI in Healthcare: Time-Series Forecasting Using Statistical, Neural, and Ensemble Architectures. Front. Big Data, 3(4), 17.
Kotu, V., & Deshpande, B. (2019). Data science concepts and practice (2 ed.). Elsevier Inc. https://doi.org/https://doi.org/10.1016/B978-0-12-814761-0.00001-0
Kravets, A. G., Gunaid, M. A., Loshmanov, V. I., Rasulov, S. S., & Lempert, L. B. (2018). Model of medicines sales forecasting taking into account factors of influence. J. Phys.: Conf. Ser., 1015(3), 8.
Kullimratchai, P. (2018). Entering into the Era of Data Science. EAU Heritage Journal Science and Technology, 12(2), 10.
Linnér, L., Eriksson, I., Persson, M., & Wettermark, B. (2020). Forecasting drug utilization and expenditure: ten years of experience in Stockholm. BMC Health Serv. Res., 20(410), 11.
Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2018). Statistical and Machine Learning forecasting methods: Concerns and ways forward. PLoS One, 13(3), 26.
Merkuryevaa, G., Valbergab, A., & Smirnov, A. (2019). Demand forecasting in pharmaceutical supply chains: A case study. Procedia Comput. Sci., 149, 8.
Ord, K., Fildes, R., & Kourentzes, N. (2017). Principles of Business Forecasting (second ed.). Wessex Press Publishing Co.
Saena, W., & Suttichaya, V. (2019, 30 October - 01 November 2019). Predicting Drug Sale Quantity Using Machine Learning. 14th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP),
Sruamsiri, R., Wagner, A. K., Ross-Degnan, D., Lu, C. Y., Dhippayom, T., Ngorsuraches, S., & Chaiyakunapruk, N. (2016). Expanding access to high-cost medicines through the E2 access program in Thailand: effects on utilisation, health outcomes and cost using an interrupted time-series analysis. BMJ Open, 6, 10, Article e008671.
Sun, J., Lin, Q., Zhao, P., Zhang, Q., Xu, K., Chen, H., Hu, C. J., Stuntz, M., Li, H., & Liu, Y. (2017). Reducing waiting time and raising outpatient satisfaction in a Chinese public tertiary general hospital-an interrupted time series study. BMC Public Health, 17, 11, Article 688.
Willmann, M., Marschal, M., Holzl, F., Schroppel, K., Autenrieth, I. B., & Peter, S. (2013). Time Series Analysis as a Tool To Predict the Impact of Antimicrobial Restriction in Antibiotic Stewardship Programs Using the Example of Multidrug-Resistant Pseudomonas aeruginosa. Antimicrob. Agents Chemother., 57, 7.
Zadeh, N. K., Sepehri, M. M., & Farvaresh, H. (2014). Intelligent Sales Prediction for Pharmaceutical Distribution Companies: A Data Mining Based Approach. Hindawi, 2014(Mathematical Problems in Engineering), 15.
Zdravkovic, M. (2019). Pharma sales data analysis and forecasting. Kaggle Inc. https://www.kaggle.com/code/milanzdravkovic/pharma-sales-data-analysis-and-forecasting/notebook