Abstract. Forecasting drug demand plays a key role in ensuring sustainable supply, effective inventory management, and timely patient access to life-saving medicines. This article presents a study of ARIMA time series and exponential smoothing methods for predicting demand for an antihypertensive blood pressure drug. The research is aimed at developing and identifying a model that ensures high accuracy and efficiency of forecasting based on selected data sets. The analysis includes the study of forecasting methods, data collection and processing, determining the optimal parameters for each method, developing a hybrid model, evaluating accuracy based on specified metrics, and analyzing the results. In the course of the conducted research, it was found that the most effective forecasting methods are time series-based approaches, including ARIMA models and exponential smoothing methods. And the developed hybrid model demonstrates high forecast accuracy by combining the advantages of the two approaches. The results show that the hybrid model is superior to ARIMA and exponential smoothing in all key metrics. Based on the findings, the introduction of hybrid models is proposed to improve the accuracy of forecasting demand in the pharmaceutical industry.

Keywords: forecasting, forecasting methods, exponential smoothing, ARIMA, time series, drug, hybrid model.