我想在 R 中模拟并获得最佳 ARIMA 第 i 次 [英] I want to simulate and obtain best ARIMA ith number of time in R

问题描述

我想用 arima.sim() 模拟 ARIMA(1,0,0) 100 次，然后用 auto.arima() 函数用于每次模拟完成.我希望程序打印每次获取ARIMA 的顺序.

I want to simulate ARIMA(1,0,0) with arima.sim() 100 times and find the best model with auto.arima() function for each time the simulation is done. I want the program to print the order of ARIMA obtain each time.

reslt = c()
num <- 60
epselon = rnorm(num, mean=0, sd=1^2)
for(i in 1:10){
reslt[i]<-auto.arima(arima.sim(n = num, model=list(ar=0.8, order = c(1, 0, 0)), n.start=1, innov=c(0,epselon[-1])))
}

以上是我尝试过但没有结果.

The above is what I tried but no result.

我想要的是将一系列ARIMA(p, d, q) 打印成10次

What I want is to print a series of ARIMA(p, d, q) into 10 times

推荐答案

这样做:

library(forecast)
nsim <- 10
result <- matrix(NA_integer_, nrow = nsim, ncol = 3)
colnames(result) <- c("p","d","q")
num <- 60
for (i in seq(nsim)) {
  result[i, ] <- arima.sim(n=num, model=list(ar=0.8, order=c(1,0,0)), sd=1) %>%
    auto.arima() %>%
    arimaorder()
}
result
#>       p d q
#>  [1,] 0 1 0
#>  [2,] 0 1 0
#>  [3,] 0 1 0
#>  [4,] 1 0 0
#>  [5,] 1 0 0
#>  [6,] 0 1 0
#>  [7,] 0 1 0
#>  [8,] 1 0 0
#>  [9,] 1 0 0
#> [10,] 1 0 0

^{由 reprex 包 (v0.3.0) 于 2020 年 6 月 24 日创建}

^{Created on 2020-06-24 by the reprex package (v0.3.0)}

一些评论:

• 您的代码每次都会生成相同的系列，因为 epselon 是在循环外生成的.由于您只是使用随机的正常创新，让 arima.sim() 像上面的代码一样处理它会更简单.
• 如果您想保留 auto.arima() 返回的整个模型对象，而不仅仅是我的代码中的订单，您可以像这样修改它:

• Your code will produce the same series every time because epselon is generated outside the loop. As you are just using random normal innovations, it is simpler to let arima.sim() handle it as in the code above.
• If you wanted to keep the whole model object that is returned by auto.arima() rather than just the orders as in my code, you could modify it like this:

library(forecast)
nsim <- 10
result <- list()
num <- 60
for (i in seq(nsim)) {
  result[[i]] <- arima.sim(n=num, model=list(ar=0.8, order=c(1,0,0)), sd=1) %>%
    auto.arima()
}
result
#> [[1]]
#> Series: . 
#> ARIMA(0,1,0) 
#> 
#> sigma^2 estimated as 1.145:  log likelihood=-87.72
#> AIC=177.44   AICc=177.51   BIC=179.52
#> 
#> [[2]]
#> Series: . 
#> ARIMA(1,0,2) with zero mean 
#> 
#> Coefficients:
#>          ar1     ma1     ma2
#>       0.5200  0.4086  0.4574
#> s.e.  0.1695  0.1889  0.1446
#> 
#> sigma^2 estimated as 0.877:  log likelihood=-80.38
#> AIC=168.77   AICc=169.5   BIC=177.15
#> 
#> [[3]]
#> Series: . 
#> ARIMA(0,1,0) 
#> 
#> sigma^2 estimated as 0.9284:  log likelihood=-81.53
#> AIC=165.05   AICc=165.12   BIC=167.13
#> 
#> [[4]]
#> Series: . 
#> ARIMA(1,0,0) with zero mean 
#> 
#> Coefficients:
#>         ar1
#>       0.615
#> s.e.  0.099
#> 
#> sigma^2 estimated as 1.123:  log likelihood=-88.35
#> AIC=180.7   AICc=180.91   BIC=184.89
#> 
#> [[5]]
#> Series: . 
#> ARIMA(0,0,3) with zero mean 
#> 
#> Coefficients:
#>          ma1     ma2      ma3
#>       0.5527  0.2726  -0.3297
#> s.e.  0.1301  0.1425   0.1202
#> 
#> sigma^2 estimated as 0.6194:  log likelihood=-69.83
#> AIC=147.66   AICc=148.39   BIC=156.04
#> 
#> [[6]]
#> Series: . 
#> ARIMA(1,0,0) with non-zero mean 
#> 
#> Coefficients:
#>          ar1    mean
#>       0.7108  0.9147
#> s.e.  0.0892  0.4871
#> 
#> sigma^2 estimated as 1.332:  log likelihood=-93.08
#> AIC=192.15   AICc=192.58   BIC=198.43
#> 
#> [[7]]
#> Series: . 
#> ARIMA(1,0,1) with non-zero mean 
#> 
#> Coefficients:
#>          ar1     ma1     mean
#>       0.6116  0.3781  -1.0024
#> s.e.  0.1264  0.1559   0.4671
#> 
#> sigma^2 estimated as 1.161:  log likelihood=-88.6
#> AIC=185.2   AICc=185.92   BIC=193.57
#> 
#> [[8]]
#> Series: . 
#> ARIMA(1,0,0) with zero mean 
#> 
#> Coefficients:
#>          ar1
#>       0.6412
#> s.e.  0.0969
#> 
#> sigma^2 estimated as 0.8666:  log likelihood=-80.6
#> AIC=165.2   AICc=165.41   BIC=169.39
#> 
#> [[9]]
#> Series: . 
#> ARIMA(0,1,0) 
#> 
#> sigma^2 estimated as 1.314:  log likelihood=-91.78
#> AIC=185.57   AICc=185.64   BIC=187.64
#> 
#> [[10]]
#> Series: . 
#> ARIMA(1,0,0) with non-zero mean 
#> 
#> Coefficients:
#>          ar1    mean
#>       0.6714  1.3449
#> s.e.  0.0985  0.4428
#> 
#> sigma^2 estimated as 1.397:  log likelihood=-94.44
#> AIC=194.89   AICc=195.32   BIC=201.17

^{由 reprex 包 (v0.3.0) 于 2020 年 6 月 24 日创建}

^{Created on 2020-06-24 by the reprex package (v0.3.0)}

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