Question

我想编写自己的预测函数，分配forecast对象，并使用该对象的预测包中的use函数。我尝试通过以下方式（使用这种方法How to create a forecast object in R）复制meanf函数：

myfun <- function(x, h, ...)
{
  # Compute some forecasts
  fc <- rep(mean(x), h)
  # Construct output list
  output <- list(mean=fc, x=x, ...)
  # Return with forecasting class
  return(structure(output, class='forecast'))
}

但是当我应用精度函数时：

# sample dataset
price <- c(351.75, 347, 348, 342, 339, 339.86, 342.61, 345, 340, 336.11, 
           331, 333.94, 330.01, 317, 313, 313.98, 315, 319.45, 313, 316, 
           316.5, 315, 320, 315, 311.23, 305.55, 298.02, 291.8, 294.98, 
           296.44, 296, 294, 290.65, 288, 291.99, 295, 310, 303.1, 306.11, 
           309.51, 312.51, 328.1, 328.1, 324.8, 329.23, 337.01, 333.6, 333, 
           327.23, 328.5, 328.54, 324.5, 322, 317.01, 318, 319.98, 329.8, 
           323, 317, 318.55, 319.98, 323.99, 316.09, 315.01, 317.5, 315.03, 
           312.55, 312, 315, 312.89, 308.5, 295.53, 308, 315, 285.12, 284.34, 
           285, 281.39, 282.92, 285.94, 284.96, 282.9, 273.5, 273.5, 273.21, 
           281.14, 286.99, 283, 280.39, 283, 280, 285, 285.02, 289, 288, 
           284.5, 280.83, 278.3, 274.1, 276)
price <- ts(price, start = 1, frequency = 1)
train <- subset(price, end = length(price) - 10)
test <- subset(price, start = (length(price) + 1) - 10)

# my forecast function
myfun <- function(x, h, ...)
{
  # Compute some forecasts
  fc <- rep(mean(x), h)
  # Construct output list
  output <- list(mean=fc, x=x, ...)
  # Return with forecasting class
  return(structure(output, class='forecast'))
}

# aplpy function and accuracy
myMean <- myfun(train, 10)
accuracy(myMean, test)

它返回错误：

NextMethod（.Generic）中的错误：无法将'tsp'分配给零长度向量

我不明白此错误？

Answer 1

问题是您的output没有适合的值元素。这很重要，因为forecast:::accuracy.default()调用forecast:::trainingaccuracy()，而后者依次调用fitted()，并尝试从时间序列对象中减去结果。当fitted()的结果为NULL时，您将收到该错误。我们可以通过修改myMean()来解决它：

myfun <- function(x, h, ...)
{
    # Compute some forecasts
    xmean <- mean(x)
    fc <- rep(xmean, h)
    fitted.values <- rep(xmean, length(x))
    # Construct output list
    output <- list(mean=fc, x=x, fitted.values=fitted.values, ...)
    # Return with forecasting class
    return(structure(output, class='forecast'))
}

# aplpy function and accuracy
myMean <- myfun(train, 10)
accuracy(myMean, test)

#                         ME     RMSE      MAE         MPE      MAPE     MASE
# Training set -1.388816e-14 19.61672 15.93970  -0.4032892  5.180507 3.718275
# Test set     -2.989633e+01 30.27324 29.89633 -10.6303518 10.630352 6.973957
#                   ACF1 Theil's U
# Training set 0.9181787        NA
# Test set     0.6992239  9.267271

尝试在预测对象上应用预测方法时出错

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