e=arima.sim(n=1000,list(order=c(0,0,0))) e1=lag(e,1) e2=lag(e,2)
时间: 2024-05-20 13:17:27 浏览: 19
These lines of code simulate a time series of 1000 observations using the ARIMA model with order (0,0,0), which is equivalent to a white noise process. The second and third lines create two new series, e1 and e2, which are the original series shifted by one and two time periods, respectively. These lagged series can be used as input variables in a regression model to capture the time dependence structure in the original series.
相关问题
e=arima.sim(n=1000,list(order=c(0,0,0))) e1=lag(e,1) e2=lag(e,2) x1=e-2*e1 x2=e-0.5*e1 x3=e-4/5*e1+16/25*e2 x4=e-5/4*e1+25/16*e2
These are four different time series models that each include a white noise error term e with different lagged terms:
- Model x1 includes a lagged error term e1 with coefficient -2.
- Model x2 includes a lagged error term e1 with coefficient -0.5.
- Model x3 includes lagged error terms e1 with coefficient -4/5 and e2 with coefficient 16/25.
- Model x4 includes lagged error terms e1 with coefficient -5/4 and e2 with coefficient 25/16.
Each of these models is an ARIMA model with a differencing order of 0 and no autoregressive or moving average terms. The lagged error terms are included as exogenous variables in the model to capture any additional dependence on past values of the error term beyond the white noise assumption. The specific lagged terms and coefficients are chosen based on some underlying theory or hypothesis about the data generating process.
model = ARIMA(train, order=order) model_fit = model.fit(disp=0)
这是使用 ARIMA 模型对时间序列数据进行建模和拟合。ARIMA 模型是一种常用的时间序列预测模型,可以用来预测未来的数值。其中,train 是训练集数据,order 是模型的参数,可以通过自相关函数和偏自相关函数来确定。model_fit 是拟合后的模型对象,可以用来进行预测和评估模型的性能。disp=0 表示不显示拟合过程中的输出。
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