在RStudio中,我在内置的乘客数据(AirPassengers)上运行auto.arima。数据似乎有乘法趋势。预测结果似乎相当准确。
# Load the corresponging library.
library(forecast)
# Save the default parameters.
defaultPar <- par(no.readonly = TRUE)
# Prepare a graph of four (2x2) subgraphs
par(mfrow = c(3, 1))
# Fit the model. Use the built in AirPassengers data.
modelAA <- auto.arima(AirPassengers)
plot(AirPassengers,
main = 'Air passengers',
col = 'purple',
ylab = 'Passengers number',
xlab = 'Year')
plot(forecast(modelAA, 24),
main = 'Air passengers + auto.arima forecasting',
col = 'red',
ylab = 'Passengers number',
xlab = 'Year')
qqnorm(modelAA$residuals, col = 'red')
qqline(modelAA$residuals, col = 'green')
# Restore the default parameters.
par(defaultPar)
如下面的代码示例所示,自动ARIMA预测选择了以下模型:(2,1,1)(0,1,0)[12]。
print(modelAA)
Series: AirPassengers
ARIMA(2,1,1)(0,1,0)[12]
Coefficients:
ar1 ar2 ma1
0.5960 0.2143 -0.9819
s.e. 0.0888 0.0880 0.0292
sigma^2 estimated as 132.3: log likelihood=-504.92
AIC=1017.85 AICc=1018.17 BIC=1029.35
如何解释自动选择(2,1,1)(0,1,0)[12]并通过调用arima(x,order = c()...)来重现它?感谢。
答案 0 :(得分:0)
季节性华丽模型。您可以从enter link description here获取详细信息。
当您致电order/sensonal/period
period = 12
时,您应将arima
设置为function (x, order = c(0L, 0L, 0L), seasonal = list(order = c(0L, 0L, 0L), period = NA), xreg = NULL, include.mean = TRUE, transform.pars = TRUE, fixed = NULL, init = NULL, method = c("CSS-ML", "ML", "CSS"), n.cond, SSinit = c("Gardner1980", "Rossignol2011"), optim.method = "BFGS", optim.control = list(), kappa = 1e+06)
。