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## Make it easy to make ensemble time series forecast

`forecastHybrid`

is an R package to make it easier to use the average predictions of ‘ensembles’ (or ‘combinations’) of time series models from Rob Hyndman’s `forecast`

package. It looks after the averaging, and also calculates prediction intervals by a conservative method that aims to redress the general over-optimism in forecasting prediction intervals.

New in version 0.3.0 is:

- Following developments in the
`forecast`

package, prediction intervals are now created for`nnetar`

objects in the ensemble. This should address one aspect of incorrect prediction intervals (e.g. issue #37). - theta models can be added (by including “
`f`

” in the`models =`

argument for`hybridModel()`

) and are indeed part of the default – so unless set otherwise,`hybridModel()`

will now fit six models. `accuracy.cvts()`

is now exported. It returns ra ange of summary measures of the cross-validated forecast accuracy for objects created by`cvts`

. Note that development on`cvts`

for`forecastHybrid`

has been independent of Hyndman’s work on a similarly purposed`tscv`

function in`forecast`

– be careful not to get the two confused.`plot.hybridModel()`

now supports`ggplot2`

graphics when the argument`ggplot = TRUE`

is passed.- Time series must be at least four observations long.
- Fixed an error where
`e.args`

was passed to`tbats`

instead of`t.args`

.

In some respects, version 0.2.0 was a more important upgrade because it introduced `cvts`

and the use of a weighted average forecast with weights for the component models chosen by cross-validation. I didn’t get around to writing a blog post when that happened.

## Installation

## Basic use

The `hybridModel`

function fits between two and six time series models to a time series, with or without `xreg`

external regressor explanatory variables. The default, if the `models=`

argument is left blank, is to fit all six available models. The modelling process also determines a set of weights to be used in subsequent forecasts. The two options are:

- equal weights ie a simple average
- cross validation weights ie a weighted average, with models that performed better in cross-validation given more weight.

Generally, we expect cross-validation weights to perform better, but they are quite computationally intensive and we haven’t got round to implementing parallel processing yet.

Here’s a demo with the median duration of unemployment in weeks from the `economics`

dataset in the `ggplot2`

package. This snippet demonstrates both ways of using weights and the default base plot method. This plot method is the one provided by `forecast`

for all objects of class `forecast`

, including those created by forecastHybrid.

The cross-validation errors should be better because they give less weight to poor performing forecasts. The forecasts from `nnetar`

for example are often not good, and in a set of experiments for a recent talk at the Australian Statistical Conference I showed that they are sufficiently poor that they often worsen ensemble forecasts when added to the mix. Setting weights by cross-validation should mitigate this.

All of the component models can be accessed individually if desired:

The `ggplot2::autoplot()`

method provided by the `forecast`

package also works:

## Theta models

We implemented the Theta forecast method based on `thetaf()`

in Hyndman’s `forecast`

(although there are alternatives). We had to split the process into a modelling function `thetam()`

, and an accompanying `forecast.thetam`

method. The results are identical to `forecast::thetaf()`

, as demonstrated below:

## Conservative prediction intervals?

The `forecastHybrid`

method of setting prediction intervals is conservative. It takes the widest range of values covered by any of the component models. In a blog post on different ways of setting forecast combinations, Rob Hyndman points out that, using the monthly `co2`

dataset of Mauna Loa Atmospheric CO2 Concentration distributed with R, the prediction intervals for the `forecastHybrid`

method with six component models look too conservative ie sufficiently wide that they look very likely to contain the correct values much more than aimed for. In this particular case I agree – the method should be used with a bit of caution.

In practice, I often use only the `auto.arima`

and `ets`

forecasts in combination rather than all six possibilities. Even then, the method is sometimes too conservative with data that of monthly or higher frequency, particularly at the 80% level. Here’s the conclusions from my recent presentation on this topic (apologies for the screenshot from PowerPoint):

So for annual and quarterly real world data, the method is not too conservative at all; and if you want your prediction intervals to contain the true value 95% of the time, the method checks out ok even for monthly data.

## Feedback and suggestions

Our preferred approach for you to provide suggestions and bug reports is to file an issue on GitHub. Other comments and feedback could be provided as a comment on this blog post or on Twitter.

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