ARIMAX versus Holt Winter Methods: The Case of Blood Demand Prediction in Thailand

APA 6th edition
In-text: (Tanyavutti & Tanlamai, 2018)
Your Bibliography: Tanyavutti, A. & Tanlamai, U. (2018). ARIMAX versus Holt Winter Methods: The Case of Blood Demand Prediction in Thailand. International Journal of Environmental and Science Education, 13(6), 519-525.

Harvard
In-text: (Tanyavutti and Tanlamai, 2018)
Your Bibliography: Tanyavutti, A. and Tanlamai, U. (2018). ARIMAX versus Holt Winter Methods: The Case of Blood Demand Prediction in Thailand. International Journal of Environmental and Science Education, 13(6), pp. 519-525.

Chicago 16th edition
In-text: (Tanyavutti and Tanlamai 2018)
Your Bibliography: Tanyavutti, Ardthawee and Uthai Tanlamai (2018). "ARIMAX versus Holt Winter Methods: The Case of Blood Demand Prediction in Thailand". International Journal of Environmental and Science Education 13 (6):519-525.

Abstract

National and Regional Blood Center are the main blood transfusion centers to acquire blood from donors and distribute to hospitals in Thailand. However, the estimation of blood availability can be difficult because of specific factors such as a very high number of blood donors during the birthday of King and Queen and a seasonal blood shortage during major holidays like the New Year. This paper focuses on statistically prediction of blood demand in Thailand. Monthly data from January 2012 to December 2015 are separated into 2 periods, 45 months for model training and 3 months for model validation. Box-Jenkin’s models with independent variables (ARIMAX) and Holt Winter’s techniques are compared to report the best model fit using the smallest value of Mean Absolute Percentage Error. The two independent variables affecting the blood demands based on geolocation are Platelet Demand and Dengue Fever Patients. The result finds that majority predictions by ARIMAX provide better model fit. R script in Tableau is used for tool development.

Keywords: Auto Regressive Integrated with Moving Average and Exogenous Variables (ARIMAX), exponential smoothing, Red Blood Cell (RBC), National Blood Center (NBC), Mean Absolute Percentage Error (MAPE)

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