Research On Forestry Output Value Based On Time Series Analysis

In recent years,the government has continuously increased public investment in forestry to promote the sustainable development of forestry ecology.Under the leadership of the government,social capital has gradually invested in forestry.This study is aimed at forestry output value data in the forestry economy,trying to explore the law of development of the forestry economy.Time series analysis method is an important method to analyze the time series of forestry output value.It is to analyze the statistical characteristics of forestry output value historical data by establishing mathematical model,and then predict the future forestry output value data.Based on the time series analysis method,the prediction model of forestry output value is constructed in China,and the dynamic characteristics of forestry output value are considered.At the same time,the periodic characteristics of output change are added to the forestry output value model,so that the output change can be better predicted.The department provides the basis for scientific and rational economic decision-making;In depth and systematic study of the characteristics of the economic cycle of forestry output value can better understand the current state of forestry economy,and then formulate corresponding counter-cyclical policies to create countermeasures for the fluctuation of forestry economy,thus promoting the gradual fluctuation of forestry economy.In this paper,the ARIMA model is constructed for the annual time series of forestry output value in China from 1952 to 2018,and the seasonal ARIMA model and GM(1,1)model are constructed for the seasonal time series of forestry output value from 2004 to 2018.The forestry output value is calculated using R language and Eviews software.The sequence is processed and modeled.Firstly,this paper studies the development characteristics of seasonal output time series of forestry production value.By analyzing the long-term trend,annual cycle and semi-annual cycle of seasonal forestry output value,the economic cycle and development trend characteristics of forestry output value are obtained.Secondly,the economic cycle of forestry output value is introduced into the ARIMA model,and the seasonal ARIMA model is established.At the same time,the GM(1,1)model is established by using the seasonal time series of forestry output value,and the seasonal ARIMA model is combined with GM(1,1)The model is a comparative study.The research shows that the time series of forestry output value in China has obvious characteristics of periodicity and volatility.It means that the forestry output value in winter is much higher than that in other seasons and the forestry output value as a whole shows the trend of fluctuation and rise.The economic cycle and volatility trend of forestry output value are introduced into the traditional ARIMA model.Through the improvement and optimization of the model,The fitting effect is better than that of the GM(1,1)model and the traditional ARIMA model.The traditional method for checking the stability of time series data is mainly ADF unit root test or KPSS test,but in practical application.In the middle,the ADF test or the unit root testare basically for the annual time series data,and when we test the stability of the seasonal forestry output value data,it is more reasonable to use the CH test.