R 如何获得回路中九（9）个子系列的精度度量

我想通过以下步骤获得9个不同子系列

的
精度度量值，如下面我的
R循环中所述：

模拟10个
AR（1）
系列样本
将序列拆分为大小为
1、2、3、4、5、6、7、8、9的子序列，不重叠

使用替换对每个块大小1000倍的子系列进行重采样
通过连接每个块大小的所有重采样子系列，形成一个新系列
检查每个区块大小新形成的九（9）个子系列的
准确性
（ME、RMSE、MAE、MPE、MAPE）

详情如下:

################################################################################
## Load packages and prepare multicore process
library(forecast)
library(future.apply)
plan(multisession)
library(parallel)
library(foreach)
library(doParallel)
n_cores <- detectCores()
cl <- makeCluster(n_cores)
registerDoParallel(cores = detectCores())

## simulate ARIMA(1,0, 0)
set.seed(289805)
N10_ar0.8_seed289805 <- arima.sim(n=10, model=list(ar=0.8, order=c(1, 0, 0)), sd=1)
auto.arima(N10_ar0.8_seed289805, ic="aicc")

########################################################
## create a vector of block sizes
t <- length(N10_ar0.8_seed289805)    # the length of the time series
lb <- seq(t-1) #seq(t-2)+1   # vector of block sizes to be 1 < l < n (i.e to be between 1 and n exclusively)
########################################################
## This section create matrix to store block means
BOOTSTRAP <- matrix(nrow = 1, ncol = length(lb))
colnames(BOOTSTRAP) <- lb
########################################################
## This section use foreach function to do detail in the brace
#set.seed(123, kind = "L'Ecuyer-CMRG")
BOOTSTRAP <- foreach(b = 1:length(lb), .combine = 'cbind', .packages = c("forecast", "Metrics")) %dopar%{
  l <- lb[b]# block size at each instance 
  m <- ceiling(t / l)                                 # number of blocks
  blk <- split(N10_ar0.8_seed289805, rep(1:m, each=l, length.out = t))  # divides the series into blocks
  ######################################################
  set.seed(5)
  res<-sample(blk, replace=T, 1000)        # resamples the blocks
  res.unlist <- unlist(res, use.names = FALSE)   # unlist the bootstrap series
  mod <- auto.arima(res.unlist)
  fit <- fitted(mod)
  ACCURACY <- forecast::accuracy(fit, res.unlist)      # RETURNS ACCURACY
  BOOTSTRAP[b] <- ACCURACY
}
BOOTSTRAPS <- matrix(BOOTSTRAP, nrow = 1, ncol = length(lb))
colnames(BOOTSTRAPS) <- lb
BOOTSTRAPS

我想得到这样的东西：

Statistic   1      2        3       4      5        6      7      8       9
----------------------------------------------------------------------------------
MSE       0.886   1.021    0.8866  0.886  1.021    0.886  0.886  1.021    0.886
MAE       0.762   0.835    0.762   0.762  0.835    0.762  0.762  0.835    0.762
MAPE      3.859   2.263    3.859   3.859  2.263    3.859  3.859  2.263    3.859
ME       -0.005   0.381   -0.005   -0.005 0.381    -0.005 -0.005 0.381    -0.005
MPE      -0.086   1.004   -0.086   -0.086 1.004    -0.086 -0.086 1.004   -0.086

精度
是一个单行矩阵：

ACCURACY
                   ME     RMSE      MAE      MPE     MAPE
Test set 0.0002838375 1.413514 1.129676 8.073587 205.3605

dim(ACCURACY)
[1] 1 5

您可以使用
rbind
而不是
cbind
并转置最终结果。

请注意，您不需要定义
BOOTSTRAP
，因为它是由
foreach
直接返回的：

################################################################################
## Load packages and prepare multicore process
library(forecast)
library(future.apply)
plan(multisession)
library(parallel)
library(foreach)
library(doParallel)
n_cores <- detectCores()
cl <- makeCluster(n_cores)
registerDoParallel(cores = detectCores())

## simulate ARIMA(1,0, 0)
set.seed(289805)
N10_ar0.8_seed289805 <- arima.sim(n=10, model=list(ar=0.8, order=c(1, 0, 0)), sd=1)
auto.arima(N10_ar0.8_seed289805, ic="aicc")

########################################################
## create a vector of block sizes
t <- length(N10_ar0.8_seed289805)    # the length of the time series
lb <- seq(t-1) #seq(t-2)+1   # vector of block sizes to be 1 < l < n (i.e to be between 1 and n exclusively)
########################################################

########################################################
## This section use foreach function to do detail in the brace
#set.seed(123, kind = "L'Ecuyer-CMRG")
BOOTSTRAP <- foreach(b = 1:length(lb), .combine = 'rbind', .packages = c("forecast", "Metrics")) %dopar%{
  l <- lb[b]# block size at each instance 
  m <- ceiling(t / l)                                 # number of blocks
  blk <- split(N10_ar0.8_seed289805, rep(1:m, each=l, length.out = t))  # divides the series into blocks
  ######################################################
  set.seed(5)
  res<-sample(blk, replace=T, 1000)        # resamples the blocks
  res.unlist <- unlist(res, use.names = FALSE)   # unlist the bootstrap series
  mod <- auto.arima(res.unlist)
  fit <- fitted(mod)
  ACCURACY <- forecast::accuracy(fit, res.unlist)      # RETURNS ACCURACY
  ACCURACY
}
BOOTSTRAPS <- t(BOOTSTRAP)
colnames(BOOTSTRAPS) <- lb
BOOTSTRAPS


                 1            2             3             4            5             6             7             8            9
ME   -1.474358e-04 2.838375e-04 -1.109465e-04 -3.546411e-04 4.991512e-05 -5.613602e-04 -2.061036e-04 -2.793918e-04  -0.00016262
RMSE  1.576266e+00 1.413514e+00  1.407372e+00  1.226815e+00 1.142930e+00  1.089578e+00  1.025063e+00  9.066126e-01   0.86546536
MAE   1.298314e+00 1.129676e+00  1.111374e+00  8.359341e-01 7.644257e-01  8.700083e-01  8.375352e-01  7.335306e-01   0.66246307
MPE  -1.210742e+00 8.073587e+00 -6.381357e+00  2.462817e+01 8.554913e+01  1.980497e+01  2.027281e+01 -2.615038e+01 -32.72766437
MAPE  2.048705e+02 2.053605e+02  1.922015e+02  1.500389e+02 1.533140e+02  1.768961e+02  1.665399e+02  1.436919e+02 140.63999983

################################################################################
##加载包并准备多核过程
图书馆（预测）
图书馆（future.apply）
计划（多段）
图书馆（平行）
图书馆（foreach）
图书馆（双平行）
n_芯
################################################################################
## Load packages and prepare multicore process
library(forecast)
library(future.apply)
plan(multisession)
library(parallel)
library(foreach)
library(doParallel)
n_cores <- detectCores()
cl <- makeCluster(n_cores)
registerDoParallel(cores = detectCores())

## simulate ARIMA(1,0, 0)
set.seed(289805)
N10_ar0.8_seed289805 <- arima.sim(n=10, model=list(ar=0.8, order=c(1, 0, 0)), sd=1)
auto.arima(N10_ar0.8_seed289805, ic="aicc")

########################################################
## create a vector of block sizes
t <- length(N10_ar0.8_seed289805)    # the length of the time series
lb <- seq(t-1) #seq(t-2)+1   # vector of block sizes to be 1 < l < n (i.e to be between 1 and n exclusively)
########################################################

########################################################
## This section use foreach function to do detail in the brace
#set.seed(123, kind = "L'Ecuyer-CMRG")
BOOTSTRAP <- foreach(b = 1:length(lb), .combine = 'rbind', .packages = c("forecast", "Metrics")) %dopar%{
  l <- lb[b]# block size at each instance 
  m <- ceiling(t / l)                                 # number of blocks
  blk <- split(N10_ar0.8_seed289805, rep(1:m, each=l, length.out = t))  # divides the series into blocks
  ######################################################
  set.seed(5)
  res<-sample(blk, replace=T, 1000)        # resamples the blocks
  res.unlist <- unlist(res, use.names = FALSE)   # unlist the bootstrap series
  mod <- auto.arima(res.unlist)
  fit <- fitted(mod)
  ACCURACY <- forecast::accuracy(fit, res.unlist)      # RETURNS ACCURACY
  ACCURACY
}
BOOTSTRAPS <- t(BOOTSTRAP)
colnames(BOOTSTRAPS) <- lb
BOOTSTRAPS


                 1            2             3             4            5             6             7             8            9
ME   -1.474358e-04 2.838375e-04 -1.109465e-04 -3.546411e-04 4.991512e-05 -5.613602e-04 -2.061036e-04 -2.793918e-04  -0.00016262
RMSE  1.576266e+00 1.413514e+00  1.407372e+00  1.226815e+00 1.142930e+00  1.089578e+00  1.025063e+00  9.066126e-01   0.86546536
MAE   1.298314e+00 1.129676e+00  1.111374e+00  8.359341e-01 7.644257e-01  8.700083e-01  8.375352e-01  7.335306e-01   0.66246307
MPE  -1.210742e+00 8.073587e+00 -6.381357e+00  2.462817e+01 8.554913e+01  1.980497e+01  2.027281e+01 -2.615038e+01 -32.72766437
MAPE  2.048705e+02 2.053605e+02  1.922015e+02  1.500389e+02 1.533140e+02  1.768961e+02  1.665399e+02  1.436919e+02 140.63999983