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Time series forecasting and application in pharmaceutical area |
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| รหัสดีโอไอ | |
| Creator | Gornganog Nettip |
| Title | Time series forecasting and application in pharmaceutical area |
| Contributor | Lawan Sratthaphut |
| Publisher | Faculty of Pharmaceutical Sciences KKU MSU UBU |
| Publication Year | 2566 |
| Journal Title | Isan Journal of Pharmaceutical Sciences |
| Journal Vol. | 19 |
| Journal No. | 3 |
| Page no. | 1-11 |
| Keyword | time series, time series forecasting, machine learning model, drug expenditure forecasting, drug demand forecasting |
| URL Website | https://tci-thaijo.org/index.php/IJPS |
| Website title | Isan Journal of Pharmaceutical Sciences, IJPS |
| ISSN | 19050852 |
| 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. |
