R 中的时间序列交叉验证：使用 csv() 和 tslm()-模型

我目前正在尝试使用时间序列交叉验证来评估 tslm 模型。我想使用固定模型（不进行参数重新估计）来查看去年评估期的 1 到 3 步预测。

我很难得到tsCV and tslm从预测库中可以很好地协同工作。我缺少什么？

library(forecast)
library(ggfortify)

AirPassengers_train <- head(AirPassengers, 100)
AirPassengers_test  <- tail(AirPassengers, 44)

## Holdout Evaluation
n_train <- length(AirPassengers_train)
n_test  <- length(AirPassengers_test)
pred_train <- ts(rnorm(n_train))
pred_test  <- ts(rnorm(n_test))

fit <- tslm(AirPassengers_train ~ trend + pred_train)

forecast(fit, newdata = data.frame(pred_train = pred_test)) %>% 
  accuracy(AirPassengers_test)
#>                        ME     RMSE      MAE       MPE     MAPE     MASE
#> Training set 1.135819e-15 30.03715 23.41818 -1.304311 10.89785 0.798141
#> Test set     3.681350e+01 76.39219 55.35298  6.513998 11.96379 1.886546
#>                   ACF1 Theil's U
#> Training set 0.6997632        NA
#> Test set     0.7287923  1.412804


## tsCV Evaluation
fc_reg <- function(x) forecast(x, newdata = data.frame(pred_train = pred_test),
                               h = h, model = fit)

tsCV(AirPassengers_test, fc_reg, h = 1)
#>      Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
#> 1957                  NA  NA  NA  NA  NA  NA  NA  NA
#> 1958  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA
#> 1959  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA
#> 1960  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA


forecast(AirPassengers_test, newdata = data.frame(pred_train = pred_test),
         h = 1, model = fit)
#> Error in forecast.ts(AirPassengers_test, newdata = data.frame(pred_train = pred_test),
#> : Unknown model class

我有一种感觉，那就是https://gist.github.com/robjhyndman/d9eb5568a78dbc79f7acc49e22553e96 https://gist.github.com/robjhyndman/d9eb5568a78dbc79f7acc49e22553e96是相关的。我如何将其应用到上面的场景中？

对于时间序列交叉验证，您应该为每个训练集拟合一个单独的模型，而不是传递现有模型。对于预测变量，该函数需要能够在拟合每个模型时获取相关元素，并在生成预测时获取其他元素。

以下将起作用。

fc <- function(y, h, xreg)
{
  if(NROW(xreg) < length(y) + h)
    stop("Not enough xreg data for forecasting")
  X <- xreg[seq_along(y),]
  fit <- tslm(y ~ X)
  X <- xreg[length(y)+seq(h),]
  forecast(fit, newdata=X)
}

# Predictors of the same length as the data
# and with the same time series characteristics.    
pred <- ts(rnorm(length(AirPassengers)), start=start(AirPassengers),
           frequency=frequency(AirPassengers))

# Now pass the whole time series and the corresponding predictors 
tsCV(AirPassengers, fc, xreg=pred)

如果您有多个预测变量，那么xreg应该是一个矩阵。