如何在R中创建预测对象 [英] How to create a forecast object in R

问题描述

我编写了一个函数，该函数使用不同的预测方法(例如forecast::nnetar，forecast::tbats，forecast::Arima和forecast::ets)来预测时间序列对象.
我知道forecastHybrid::hybridModel函数正在执行此操作，我只想创建更自定义的内容.因此，我现在返回包含result$mean，result$error和result$fit的列表.我想使用精度或绘图功能，例如预测对象.有没有简单的方法可以做到这一点?还是太复杂了?

I have wrote a function that forecasts a time series object using different forecast methods like forecast::nnetar, forecast::tbats, forecast::Arima and forecast::ets.
I know that forecastHybrid::hybridModel function is doing this, I just wanted to create something more customized. So I am now returning a list with result$mean, result$error and result$fit. I want to use accuracy or plot function like forecast object. Is there a easy way to do that? or is it too complicated?

关于该函数，它使用一个ts对象，一个Arima模型(我通过进行差异处理并检查ACF-PACF图找到了该模型)以及要预测的地平线.它适用于nnetar，ets，tbats和我的Arima模型.
它结合了拟合度和预测值，并创建了新的拟合值和预测值.
它返回一个列表对象，该列表对象的预测值为result$mean(在预测对象中也是如此)，拟合值作为result$fit，错误值为result$error.
现在，有了该返回的对象，我就无法自动化一系列工作，例如具有准确性，绘制地块等.因此，如果可能的话，我想返回一个预测对象.就是这样.

About the function, it takes a ts object, an arima model-that I found by taking differences and checking ACF-PACF graphs- and a horizon to be forecasted. It applies nnetar, ets, tbats and my arima model.
It combines their fits and forecasts and create a new fit values and forecast values.
It returns a list object with forecasted values as result$mean (this is also same in forecast objects), fitted values as result$fit and errors as result$error.
Now with that returned object, I cant automate some series of works like having accuracy, making plots etc. So I want to return a forecast object if it is possible. that is what it is.

推荐答案

预测对象只是一个列表，其中包含一些项目，并具有预测"类.查看任何现有功能，以了解如何进行.这是一个非常简单的模板:

A forecast object is just a list containing a few items, and given a class "forecast". Look at any of the existing functions to see how to do it. Here is a very simple template:

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

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