Application of time series to groundwater level prediction
Hang Dong-wei · Journal of tianjin University of Technology · 2008
In this paper it builds up a forecasting model,which is combined with the ordinary linear regression(least square estimate) model,non-parametric season estimation and time series analysis.The proposed model can well separate the trend component of the data and depict the smooth season component of every month.It eliminates trend component and season component's influences in the data,dealing into smooth and zero expectative data with the new data and determines appropriate model.By analyzing the data of Beijing's ground water level data from 1990 to 1994,it defines as the ARMA(1,4) model from AC and PAC's characters and the selection standards.Through computing the data,it can forecast Beijing city's ground water level in 1995.It indicates the result that the ideas have the more precision through RSS.