arima.sim(n = 1000, list(ar = c(-0.8, 0.5), ma = -1.1)) : 模型的'ar'部分不平穩

翻译：arima.sim(n = 1000, list(ar = c(-0.8, 0.5), ma = -1.1)) ：模型的'ar'部分不平稳。

该函数尝试使用 ARIMA 模型来模拟时间序列数据，但是由于 AR 部分不平稳，因此模拟失败。这可能是由于 AR 部分包含非平稳性或趋势导致的。需要重新考虑模型或先对数据进行适当的差分或转换。

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.

ARIMA(2,1,2)(0,0,2)[12]的模型方程，参数分别为ar1=0.8682,ar2=-0.8544,ma1=-0.7178,ma2=0.6628,sma1=-0.2211,sma2=-0.1482

根据ARIMA(2,1,2)(0,0,2)[12]的模型方程和给定的参数，可以将模型方程具体表示为：

(1-B)(1-B^12)(Y_t - Y_{t-1}) = (1 - 0.8682B - 0.8544B^2)(ε_t + 0.7178ε_{t-1} - 0.6628ε_{t-2} - 0.2211ε_{t-12} - 0.1482ε_{t-13})

其中，B表示向后移动一期的算子，Y_t表示时间为t的观测值，ε_t表示时间为t的白噪声随机变量，0.8682、-0.8544、0.7178、-0.6628、-0.2211、-0.1482为模型参数。

需要注意的是，由于ARIMA模型是一种线性模型，因此这里的乘法实际上是指卷积运算，而不是普通的乘法。此外，参数的精度也可能会影响模型的准确性，需要根据具体情况进行调整。