Comparative Analysis of Single Exponential Smoothing and Holt's Method for Quality of Hospital Services Forecasting in General Hospital
Keywords:hospital services, hospitals, Single Exponential Smoothing, Holt's method, MAD
The quality health service is one of the basic necessities of any person or customer. To predict the number of goods can be done in a way predicted. The comparison method of Single Exponential Smoothing and Holt's method is used to predict the accuracy of inpatient services will be back for the coming period. Single Exponential Smoothing the forecasting methods used for data stationary or data is relatively stable. Holt's method is used to test for a trend or data that has a tendency to increase or decrease in the long term. The outcome of this study is the Single Exponential Smoothing method is more precise than Holt's method because of the history of hospitalized patients who do not experience an increase or no trend. In addition, the percentage of error (the difference between the actual data with the forecast value) and Mean Absolute Deviation (MAD) to calculate the forecast error obtained from the Single Exponential Smoothing method is smaller compared to Holt's method.
S. Tsumoto and S. Hirano, "Analytics for hospital management," ACM Int. Conf. Proceeding Ser., vol. 07-09-Ocob, 2015.
E. C. Mavratzotis, C. J. Stathi, and S. P. Papanastasiou, "Interactive doctor-patient system of the pain relief cabinet," p. 1, 2008. https://doi.org/10.1145/1389586.1389649
S. Haldar, S. R. Mishra, M. Khelifi, A. H. Pollack, and W. Pratt, "Opportunities and design considerations for peer support in a hospital setting," Conf. Hum. Factors Comput. Syst. - Proc., vol. 2017-May, pp. 867-879, 2017. https://doi.org/10.1145/3025453.3026040
Y. Kurniawan, "Knowledge management model for hospital (a case study approach: Focus on knowledge gathering process)," ACM Int. Conf. Proceeding Ser., no. 9, pp. 112-116, 2017.
S. R. Mishra et al., "Supporting collaborative health tracking in the hospital: Patients' perspectives," Conf. Hum. Factors Comput. Syst. - Proc., vol. 2018-April, pp. 1-14, 2018. https://doi.org/10.1145/3173574.3174224
H. Konno and H. Yamazaki, "Mean-Absolute Deviation Portfolio Optimization Model and Its Applications to Tokyo Stock Market," Manage. Sci., vol. 37, no. 5, pp. 519-531, May 1991. https://doi.org/10.1287/mnsc.37.5.519
H. Konno and T. Koshizuka, "Mean-absolute deviation model," IIE Trans. (Institute Ind. Eng., vol. 37, no. 10, pp. 893-900, Oct. 2005. https://doi.org/10.1080/07408170591007786
C. Willmott and K. Matsuura, "Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance," Clim. Res., vol. 30, no. 1, pp. 79-82, Dec. 2005. https://doi.org/10.3354/cr030079
H. C. Shin and A. H. Sayed, "Mean-Square Performance of a Family of Affine Projection Algorithms," IEEE Trans. Signal Process., vol. 52, no. 1, pp. 90-102, Jan. 2004. https://doi.org/10.1109/TSP.2003.820077
A. P. Slavia, E. Sutoyo, and D. Witarsyah, "Hotspots Forecasting Using Autoregressive Integrated Moving Average (ARIMA) for Detecting Forest Fires," in 2019 IEEE International Conference on Internet of Things and Intelligence System (IoTaIS), 2019, pp. 92-97. https://doi.org/10.1109/IoTaIS47347.2019.8980400
P. Guan, D. S. Huang, and B. Sen Zhou, "Forecasting model for the incidence of hepatitis A based on artificial neural network," World J. Gastroenterol., vol. 10, no. 24, pp. 3579-3582, Dec. 2004. https://doi.org/10.3748/wjg.v10.i24.3579
I. Tolstikova, M. Shubinskiy, and B. Nizomutdinov, "Hospital websites as a tool of health service," ACM Int. Conf. Proceeding Ser., vol. 2015-Novem, pp. 170-173, 2015. https://doi.org/10.1145/2846012.2846045
S. Tonjang and N. Thawesaengskulthai, "Patient food delivery error in the hospital: A case study in Thailand," ACM Int. Conf. Proceeding Ser., pp. 151-156, 2019. https://doi.org/10.1145/3335550.3335562
M. M. Ulkhaq et al., "Evaluating hospital service quality: A combination of the AHP and TOPSIS," ACM Int. Conf. Proceeding Ser., pp. 117-124, 2018. https://doi.org/10.1145/3239438.3239448
C. Klangrahad, "Evaluation of Thailand's regional hospital efficiency: An application of data envelopment analysis," ACM Int. Conf. Proceeding Ser., vol. Part F1312, pp. 104-109, 2017.
D. Risteski, A. Kulakov, and D. Davcev, "Single exponential smoothing method and neural network in one method for time series prediction," in 2004 IEEE Conference on Cybernetics and Intelligent Systems, 2004, pp. 740-744.
E. Cadenas, O. A. Jaramillo, and W. Rivera, "Analysis and forecasting of wind velocity in chetumal, quintana roo, using the single exponential smoothing method," Renew. Energy, vol. 35, no. 5, pp. 925-930, May 2010. https://doi.org/10.1016/j.renene.2009.10.037
S. Makridakis, S. Wheelwright, and R. J. Hyndman, "Forecasting: Methods and Applications, 3rd Edition," John Wiley & Sons, 1998.
M. Ordu, E. Demir, and C. Tofallis, "A comprehensive modelling framework to forecast the demand for all hospital services," Int. J. Health Plann. Manage., vol. 34, no. 2, pp. e1257-e1271, Apr. 2019. https://doi.org/10.1002/hpm.2771
S. Gelper, R. Fried, and C. Croux, "Robust forecasting with exponential and Holt-Winters smoothing," J. Forecast., vol. 29, no. 3, p. n/a-n/a, Apr. 2009. https://doi.org/10.1002/for.1125
E. S. Gardner and D. G. Dannenbring, "Forecasting With Exponential Smoothing: Some Guidelines For Model Selection," Decis. Sci., vol. 11, no. 2, pp. 370-383, Apr. 1980. https://doi.org/10.1111/j.1540-5915.1980.tb01145.x
E. M. de Oliveira and F. L. Cyrino Oliveira, "Forecasting mid-long term electric energy consumption through bagging ARIMA and exponential smoothing methods," Energy, vol. 144, pp. 776-788, Feb. 2018. https://doi.org/10.1016/j.energy.2017.12.049
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