fable | Tidy time series forecasting | Predictive Analytics library

The namespace is just Reactstrap, so this should work:

Without a reproducible example, it is hard to know what has happened here.

To take account of annual seasonality in an ARIMA model for daily data, you would be much better off using Fourier terms than trying to add seasonal ARIMA terms with a large period. For a start, seasonal ARIMA components need an integer period. Also, they use lagged values at multiples of the seasonal period, and referencing observations a year (or several years) in the past is a very inefficient way to handle seasonality. Finally, the estimation is exceedingly slow (or will return an error) for ARIMA models with large seasonal periods.

There is an example that uses Fourier terms with half-hourly electricity demand data at https://otexts.com/fpp3/complexseasonality.html#example-electricity-demand. You could easily adapt that to your situation with daily data.

forecast::auto.arima() requires a ts object. That is a vector with some time series attributes including the seasonal frequency. When you just pass a numeric vector, as you have here, it assumes the seasonal frequency is 1 (as for annual data) and will not fit a seasonal ARIMA model.

On the other hand, the tsibble object contains a time index (in this case it looks like it is a date variable) and ARIMA() will use that index to determine what type of seasonality (if any) is present. With a date variable, it will select seasonal frequency of 7 to correspond to a time of week seasonality.

To get the same thing with forecast::auto.arima(), use

To install from sources, you will need a GNU Fortran compiler installed. On Ubuntu this can be done with sudo apt install gfortran.

Alternatively, you can install a precompiled binary from the RStudio Package Manager - some guidance on how this can be done can be found here: https://packagemanager.rstudio.com/client/#/repos/1/overview

You are almost there:

You are mixing 2 different ways of doing forecasts. you either use fable or you use forecast. auto.arima is from the forecast package. Though it does work with fable, it is better to keep everything to the same package eco system. Fable is the successor of forecast. Your library loading problably conflicted somewhere.

For arima forecasts check out chapter 9.7 from Forecasting: Principles and Practice 3rd edition.

I adjusted your code to work with fable. I have included 2 ways of doing this. My preference is the second one, because then you can see the difference in AICc values and see that they are very close to each other.

A couple of things to consider:

• While it's definitely desirable to quickly evaluate the performance of many combination models, it's pretty impractical. The best option would be to evaluate your models individually, and then create a more simple combination using, e.g. the 2 or 3 best ones
• As an example, consider that you can actually have weighted combinations - e.g. 0.75 * ets + 0.25 * arima. The possibilities are now literally endless, so you start to see the limitations of the brute-force method (N.B. I don't think fable actually supports these kind of combinations yet though).

That, said, here's one approach you could use to generate all the possible combinations. Note that this might take a prohibitively long time to run - but should give you what you're after.

There are some options to read the output

1. Write the output of capture.output to a file

We could use a lambda function