使用“笔图"绘制“预测"预测 [英] Plotting `forecast` prediction using `dygraphs`

问题描述

我想使用dygraphs绘制forecast包时间序列模型的预测. 文档建议采用以下方法进行实际预测:

I want to plot a forecast package time series model's predictions using dygraphs. The documentation suggests the following approach for predictions with actuals:

hw <- HoltWinters(ldeaths)
p <- predict(hw, n.ahead = 36, prediction.interval = TRUE)
all <- cbind(ldeaths, p)

dygraph(all, "Deaths from Lung Disease (UK)") %>%
  dySeries("ldeaths", label = "Actual") %>%
  dySeries(c("p.lwr", "p.fit", "p.upr"), label = "Predicted")

导致:

关于绘制对象all的有趣之处在于其类:

The interesting thing about the plotted object all is its class:

> class(all) [1] "mts" "ts" "matrix"

> class(all) [1] "mts" "ts" "matrix"

> is.mts(all)
[1] TRUE
> is.ts(all)
[1] TRUE
> is.matrix(all)
[1] TRUE

str提供有关对象all的更多信息:

str provides a little more information about the object all:

> str(all)
 Time-Series [1:108, 1:4] from 1974 to 1983: 3035 2552 2704 2554 2014 ...
 - attr(*, "dimnames")=List of 2
  ..$ : NULL
  ..$ : chr [1:4] "ldeaths" "p.fit" "p.upr" "p.lwr"

更多检查显示all是一个数组:

More inspection shows that all is an array:

> tail(all)
         ldeaths     p.fit    p.upr     p.lwr
Jul 1982      NA 1128.3744 1656.127  600.6217
Aug 1982      NA  948.6089 1478.090  419.1282
Sep 1982      NA  960.1201 1491.429  428.8112
Oct 1982      NA 1326.5626 1859.802  793.3235
Nov 1982      NA 1479.0320 2014.306  943.7583
Dec 1982      NA 1929.8349 2467.249 1392.4206
> dim(all)
[1] 108   4
> is.array(all)
[1] TRUE

我无法使用forecast包中的预测来创建此类对象

I am unable to create this type of object using predictions from the forecast package

使用我的forecast模型unemp.mod创建预测:

With my forecast model unemp.mod I create predictions:

> f <- forecast(unemp.mod)
> f
         Point Forecast    Lo 80    Hi 80    Lo 95    Hi 95
Apr 2017       4.528274 4.287324 4.769224 4.159773 4.896775
May 2017       4.515263 4.174337 4.856189 3.993861 5.036664
Jun 2017       4.493887 4.055472 4.932303 3.823389 5.164386
Jul 2017       4.479992 3.936385 5.023599 3.648617 5.311367
Aug 2017       4.463073 3.807275 5.118871 3.460116 5.466030

虽然看起来与示例中的数组相似，但它是一个完全不同的对象:

While it looks similar to the array in the example, it's a totally different object:

> class(f)
[1] "forecast"
> str(f)
List of 10 <truncated>

如果像示例中那样我尝试使用基数R的predict生成预测，那么我还会看到一个列表对象:

If I try to generate the forecast using base R's predict like in the example, I also wind up with a list object:

> predict(unemp.mod, n.ahead = 5, prediction.interval = TRUE)
$pred
          Apr      May      Jun      Jul      Aug
2017 4.528274 4.515263 4.493887 4.479992 4.463073

$se
           Apr       May       Jun       Jul       Aug
2017 0.1880140 0.2660260 0.3420974 0.4241788 0.5117221

有人对基于forecast模型预测如何使用dygraphs创建正确的对象进行绘制有任何建议吗?

Does anyone have any suggestions on how to create the right object to plot using dygraphs based on forecast model predictions?

推荐答案

在进一步研究forecast(model)生成的列表时，我注意到实际值和点预测均以ts对象的形式给出，上下界在与dygraphs HoltWinters示例相同的数组格式.我创建了一个函数，该函数创建用于绘制假设forecast_obj <- forecast(model)所需的数组.

Upon further investigation of the list generated by forecast(model) I noticed the actuals and point forecasts are given as ts objects and the upper and lower bounds are in the same array format as the dygraphs HoltWinters example. I created a function that creates the array needed for plotting supposing forecast_obj <- forecast(model).

gen_array <- function(forecast_obj){

  actuals <- forecast_obj$x
  lower <- forecast_obj$lower[,2]
  upper <- forecast_obj$upper[,2]
  point_forecast <- forecast_obj$mean

  cbind(actuals, lower, upper, point_forecast)
}

请注意，上下限是二维数组.由于dygraphs不支持一个以上的预测间隔，因此我只选择一对(95％).

Note that the lower and upper bounds are 2 dimensional arrays. Since dygraphs does not support more than one prediction interval I only pick one pair (the 95%).

然后我使用类似这样的图形绘制结果数组:

I then plot the resulting array using something like this:

dygraph(ts_array, main = graph_title) %>% 
      dyRangeSelector() %>% 
      dyRangeSelector(height = 40,
                      dateWindow = c("2011-04-01", "2019-4-01")) %>%
      dySeries(name = "actuals", label = "actual") %>%
      dySeries(c("lower","point_forecast","upper"), label = "Predicted") %>%
      dyLegend(show = "always", hideOnMouseOut = FALSE) %>%
      dyHighlight(highlightCircleSize = 5,
                  highlightSeriesOpts = list(strokeWidth = 2)) %>%
      dyOptions(axisLineColor = "navy", gridLineColor = "grey")

此图的结果:

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