Smoothing of time series and prediction methods
Xiaoxian Liu · Chinese Journal of Hospital Statistics · 2006
Objective To predict the data on the points in time with irregular fluctuation. Methods Prediction is made in two phases. In the first phase, we predict the data at the points in time with irregular fluctuation. In the second phase, we make a formal prediction by replacing the original data with the prediction result in the first phase. Results If we predict the season tendency with the actual inpatient number in 2003, which was affected because of SARS, the result did not conform to the law. But after smoothing, we can get a more reliable season tendency. Conclusion When irregular fluctuation happens, first of all, we should deal with the data at the points in time with irregular fluctuation, and then we can make a prediction.