Analysis and forecasting for non-stationary time series of pest occurrence degree based on wavelet decomposition
Yingzhi Liu · 2011
Wavelet decomposition was applied to analyze and forecast for non-stationary time series of pest occurrence degree.The non-stationary time series was decomposed into several stationary components with wavelet decomposition.Then,every stationary component was analyzed by auto-regressive moving average method and a model was established.Finally,the models of all stationary components were combined to obtain the model of the original non-stationary time series.The occurrence degree data series of Ostrinia furnacalis in Yantai from 1959 to 2004 was used to establish forecasting model,and the data from 2005 to 2009 was used to test the model.The test result showed that the forecasting accuracy of five years reached satisfying 80%.