| dc.description.abstract | Pharmaceutical industry is instrumental to the well-being of people around the world as it deals
with the discovery, development, production and marketing of drugs and medications, which are
one of life’s necessities. Recently, there is an emerging phenomenon that some hospitals must
suppress their operation due to the deficiency of medical supply, and thus affecting patients who
are in the need for treatment. One of the factors contributing to the situation is the incapability of
providing these commodities on time from pharmaceutical companies, which results from their
inaccurate forecasting. This thesis investigates the application of machine learning algorithms in
time series forecasting, specifically focusing on Support Vector Regression (SVR) to improve
forecast accuracy. It evaluates the performance of five time series models, including Moving
Average, ARIMA, Exponential Smoothing, Holt’s Trend, and Holt’s Winters, and three types of
SVR, including Linear kernel, Polynomial kernel, and Radial Basic Function (RBF). The
evaluation is based on the Mean Absolute Percentage Error (MAPE) metric, which measures the
accuracy of the forecasts. The study then proposes hybrid models that combine SVR with
traditional time series models via ensemble methods and compares their performance with the
individual models. Finally, a case study of a pharmaceutical company is performed to assess the
impacts of these methods. The results demonstrate that the hybrid ensemble method achieves
lower MAPE values compared to the individual models. These findings offer promising insights
and highlight the potential of the proposed hybrid models in improving forecast accuracy. | en_US |
