对向量的每个元素做，就像我对R中的一个元素做的一样

下面的

R

代码按步骤执行以下算法：

模拟

ARIMA（1,1,0）

时间序列（第1行到第9行）

确定要使用的块大小向量（第19行）

选择向量的第一个元素（2）（第20行）

将时间序列拆分为大小相等的2个块（第22行）

随机对每个块重新采样1000次（第25行）

将重新采样的序列重新排列为时间序列数据（第27行）

获取重采样时间序列的RMSE（第29行）

将步骤5到步骤7循环十（10）次，并获得10个RMSE的平均值（第30行到第32行）

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如果我正确理解了您试图执行的操作，请在vector

li

中的元素之间重复这些步骤。可能有更有效的方法来完成同样的事情，特别是对于大的n值。我选择了n=5。我创建了一个矩阵'RSMEblk'来存储块的意思。如果还需要这些值，可以选择创建一个列表来存储单个块

# simulate arima(1,1,0)
library(forecast)
set.seed(100)
wn <- rnorm(10, mean = 0, sd = 1)
ts <- wn[1:2]
for (i in 3:10){
     ts<-arima.sim(n=10,model=list(ar=-0.7048,order=c(1,1,0)),start.innov=4.1,n.start=1,innov=wn)
}
ts <-ts[-1]

# write the function for RMSE
rmse <- function(x) {
     m <- auto.arima(x)
     acu <- accuracy(m)
     acu[1, 2]
}
#
n<-5 # max block size
t<-length(ts)# the length of the time series
li <- seq(n-2)+1 # vector of block sizes to be 1 < l < n (i.e to be between 1 and n exclusively)

RMSEblk<-matrix(nrow = 1, ncol = length(li))#vector to store block means
colnames(RMSEblk)<-li
for (b in 1:length(li)){
    l<- li[b]# block size
    m <- ceiling(t / l) # number of blocks
    blk<-split(ts, rep(1:m, each=l, length.out = t)) # divides the series into blocks
    singleblock <- vector() #initialize vector to receive result from for loop
    for(i in 1:10){
        res<-sample(blk, replace=T, 1000) # resamples the blocks
        res.unlist<-unlist(res, use.names = F) # unlist the bootstrap series
        tsunlist<-ts(res.unlist) # turns the bootstrap series into time series data
        # use the RMSE function
        RMSE <- rmse(tsunlist)
        singleblock[i] <- RMSE # Assign RMSE value to final result vector element i
    }
#singleblock
RMSEblk[b]<-mean(singleblock) #store into matrix
}

---

如果你喜欢我的问题，那就大惊小怪吧

# 2    3     4    5    6    7    8    9 
# ... ...  ...   ...  ...  ...  ...  ...  

# simulate arima(1,1,0)
library(forecast)
set.seed(100)
wn <- rnorm(10, mean = 0, sd = 1)
ts <- wn[1:2]
for (i in 3:10){
     ts<-arima.sim(n=10,model=list(ar=-0.7048,order=c(1,1,0)),start.innov=4.1,n.start=1,innov=wn)
}
ts <-ts[-1]

# write the function for RMSE
rmse <- function(x) {
     m <- auto.arima(x)
     acu <- accuracy(m)
     acu[1, 2]
}
#
n<-5 # max block size
t<-length(ts)# the length of the time series
li <- seq(n-2)+1 # vector of block sizes to be 1 < l < n (i.e to be between 1 and n exclusively)

RMSEblk<-matrix(nrow = 1, ncol = length(li))#vector to store block means
colnames(RMSEblk)<-li
for (b in 1:length(li)){
    l<- li[b]# block size
    m <- ceiling(t / l) # number of blocks
    blk<-split(ts, rep(1:m, each=l, length.out = t)) # divides the series into blocks
    singleblock <- vector() #initialize vector to receive result from for loop
    for(i in 1:10){
        res<-sample(blk, replace=T, 1000) # resamples the blocks
        res.unlist<-unlist(res, use.names = F) # unlist the bootstrap series
        tsunlist<-ts(res.unlist) # turns the bootstrap series into time series data
        # use the RMSE function
        RMSE <- rmse(tsunlist)
        singleblock[i] <- RMSE # Assign RMSE value to final result vector element i
    }
#singleblock
RMSEblk[b]<-mean(singleblock) #store into matrix
}

> RMSEblk
             2        3         4
[1,] 0.4671414 0.792863 0.4482386