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My master thesis is in health forecasting and I'm using R (fable, fabletools, fasster) to implement the methods. For the theoretical part of the thesis, I need to know the heuristics and the theoretical basis of each function I use. I have been using Forecasting: Principles and Practice by Rob J Hyndman and George Athanasopoulos and I have already read R documentation on these functions but I still have some doubts.

I need information like what theoretical method they follow (ARIMA, Moving Averages, ANN, etc), the mathematical expression they use and how it is decided which is the best fit (for automatic methods): I use the following methods and gathered some information about each one. I'm new in this field and I need some help. Is this correct? Can anyone add anything else about any of the functions?

ARIMA() - MSARIMA model (meaning an ARIMA model that is sensible to seasonality and can take into account several external regressors:
SNAIVE()- Linear regression with seasonality;
NNETAR() - ANN model;
fasster()
ETS()

Thank you in advance!

Diana Serrano
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  • Most of that is in the book you cite. Can you be more specific about what you haven't found there? – Rob Hyndman Apr 09 '20 at 10:21
  • @RobHyndman For example, the fasster function follows any "standard" method like moving average? For ETS (Error, Trend, Seasonal) there are many possible combinations so, the function tries every possible combination and selects the one with lower AIC, AICc, and BIC? Or just one of these measures is used? The same principle of choice applies to the other functions ( ARIMA, SNAIVE, NNETAR and fasster)? Thank you for your time. – Diana Serrano Apr 13 '20 at 09:22

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The book you cite contains information on how SNAIVE, NNETAR, ETS, and ARIMA forecasts are calculated. It explains that for model classes such as ETS and ARIMA, the AICc is used to select a particular model. It gives equations for all these methods. Please read it.

fasster() is a new method that is not fully documented yet. The readme file (https://github.com/tidyverts/fasster) provides some information, and there is a talk by the author (https://www.youtube.com/watch?v=6YlboftSalY) explaining the state space modelling framework behind it.

Rob Hyndman
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