预测R中的Holt-Winters（初始值）

Question

执行预测时，我遇到了问题。

w=ts(mydat$age,frequency = 12,start=c(1946,1))
rainseriesforecasts <- HoltWinters(w, beta=FALSE, gamma=FALSE)
rainseriesforecasts
rainseriesforecasts$fitted

此代码不会预测初始值，它只是输出了这个。

> rainseriesforecasts$fitted
             xhat    level
Feb 1946 26.66300 26.66300
Mar 1946 25.27676 25.27676
Apr 1946 26.02494 26.02494

此代码使用80和95 CI

预测下一个值（后续期间）

w=ts(mydat$age,frequency = 12,start=c(1946,1))
library("forecast")
 m <- stats::HoltWinters(w)

  p = predict(m)
  pp = stats:::predict.HoltWinters(m)
  p
  forecast(m)
  test=forecast:::forecast.HoltWinters(m,h=24)
         Point Forecast    Lo 80    Hi 80    Lo 95    Hi 95
Jan 1960       27.30020 26.32517 28.27523 25.80902 28.79139
Feb 1960       25.92822 24.83950 27.01694 24.26316 27.59327
Mar 1960       29.04779 27.85040 30.24518 27.21654 30.87905

如何使用Holt-Winters计算具有80％和95％CI的数据集初始值的预测？ 在输出中，我期待下一步：

                 xhat   level   point    80 80  95  95
Feb 1946    26.66300    26.66300    28  28  26  29  29
Mar 1946    25.27676    25.27676    27  30  28  27  25
Apr 1946    26.02494    26.02494    28  29  25  25  25



mydat
structure(list(age = c(26.663, 23.598, 26.931, 24.74, 25.806, 
24.364, 24.477, 23.901, 23.175, 23.227, 21.672, 21.87, 21.439, 
21.089, 23.709, 21.669, 21.752, 20.761, 23.479, 23.824, 23.105, 
23.11, 21.759, 22.073, 21.937, 20.035, 23.59, 21.672, 22.222, 
22.123, 23.95, 23.504, 22.238, 23.142, 21.059, 21.573, 21.548, 
20, 22.424, 20.615, 21.761, 22.874, 24.104, 23.748, 23.262, 22.907, 
21.519, 22.025, 22.604, 20.894, 24.677, 23.673, 25.32, 23.583, 
24.671, 24.454, 24.122, 24.252, 22.084, 22.991, 23.287, 23.049, 
25.076, 24.037, 24.43, 24.667, 26.451, 25.618, 25.014, 25.11, 
22.964, 23.981, 23.798, 22.27, 24.775, 22.646, 23.988, 24.737, 
26.276, 25.816, 25.21, 25.199, 23.162, 24.707, 24.364, 22.644, 
25.565, 24.062, 25.431, 24.635, 27.009, 26.606, 26.268, 26.462, 
25.246, 25.18, 24.657, 23.304, 26.982, 26.199, 27.21, 26.122, 
26.706, 26.878, 26.152, 26.379, 24.712, 25.688, 24.99, 24.239, 
26.721, 23.475, 24.767, 26.219, 28.361, 28.599, 27.914, 27.784, 
25.693, 26.881, 26.217, 24.218, 27.914, 26.975, 28.527, 27.139, 
28.982, 28.169, 28.056, 29.136, 26.291, 26.987, 26.589, 24.848, 
27.543, 26.896, 28.878, 27.39, 28.065, 28.141, 29.048, 28.484, 
26.634, 27.735, 27.132, 24.924, 28.963, 26.589, 27.931, 28.009, 
29.229, 28.759, 28.405, 27.945, 25.912, 26.619, 26.076, 25.286, 
27.66, 25.951, 26.398, 25.565, 28.865, 30, 29.261, 29.012, 26.992, 
27.897)), .Names = "age", class = "data.frame", row.names = c(NA, 
-168L))

积