Title :
Prediction method of the transformed data
Author :
Sheng, Yao ; Zheng, Xiaogu ; Wu, Guocan ; Li, Yong
Author_Institution :
Sch. of Math. Sci., Beijing Normal Univ., Beijing, China
Abstract :
In Meteorology, we often get data products we need by doing statistical analysis to observation data. It´s very important for meteorology forecasting and disaster warning. When we do statistic modeling, the data are not always normal distribution, so we transform the data to a new one first and then modeling. In this paper, we mainly consider how to forecast origin variable from experience model forecast value. On this question, people always consider less about the error from inverse transformation. We mainly use Monte Carlo method, which can be used to deal with kinds of transform function. In the third part, I have simulated logarithmic transformation to compare RMSE of Monte Carlo method and inverse. It can be see that Monte Carlo can decrease origin variable´s prediction error.
Keywords :
Monte Carlo methods; atmospheric techniques; disasters; meteorology; statistical analysis; Monte Carlo method; data products; disaster warning; logarithmic transformation; meteorology forecasting; model forecast value; observational data; statistical analysis; statistical modeling; transformed data prediction method; Data models; Educational institutions; Monte Carlo methods; Smoothing methods; Transforms; Weather forecasting; Monte Carlo; logit transform; origin variable forecast; relative humidify;
Conference_Titel :
Electrical and Control Engineering (ICECE), 2011 International Conference on
Conference_Location :
Yichang
Print_ISBN :
978-1-4244-8162-0
DOI :
10.1109/ICECENG.2011.6058256