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# Preface

1. ETS;
2. ARIMA;
3. Regression;
4. TVP regression;
5. Combination of (1), (2) and either (3), or (4);
6. Automatic selection / combination of states for ETS;
7. Automatic orders selection for ARIMA;
8. Variables selection for regression part;
9. Normal and non-normal distributions;
10. Automatic selection of most suitable distributions;
11. Multiple seasonality;
12. Occurrence part of the model to handle zeroes in data (intermittent demand);
13. Handling uncertainty of estimates of parameters.

All these extensions are needed in order to solve specific real life problems, so we will have examples and case studies later in the book, in order to see how all of this can be used. The adam() function from smooth package implements ADAM and supports the following features:

1. Model diagnostics using plot() and other methods;
2. Confidence intervals for parameters of models;
3. Automatic outliers detection;
4. Handling missing data;
5. Fine tuning of persistence vector (smoothing parameters);
6. Fine tuning of initial values of the state vector (e.g. level / trend / seasonality);
7. Two initialisation options (optimal / backcasting);
8. Advanced and custom loss functions;
9. Provided ETS, ARMA and regression parameters;
10. Fine tuning of optimiser (selection of optimisation algorithm and convergence criteria);

This textbook uses two packages from R, namely greybox, which focuses on forecasting using regression models, and smooth, which implements Single Source of Error (SSOE) state space models for purposes of time series analysis and forecasting. The textbook focuses on explaining how ADAM (“ADAM is Dynamic Adaptive Model” - recursive acronym), one of the smooth functions (introduced in v3.0.0) works, also showing how it can be used in practice with examples from R.

If you want to run examples from the textbook, two packages are needed :

install.packages("greybox")
install.packages("smooth")

Some explanations of functions from the packages are given in my blog: Package greybox for R, Package smooth for R.

A very important thing to note is that this textbook does not use tidyverse packages. I like base R, and, to be honest, I am sure that tidyverse packages are great, but I have never needed them in my research. So, I will not use pipeline operators, tibble or tsibble objects and ggplot2. It is assumed throughout the textbook that you can do all those nice tricks on your own if you want to.

You can use the following to cite the online version of this book:

• Svetunkov, I. (2021) Forecasting and Analytics with ADAM: Lancaster, UK. openforecast.org/adam. Accessed on [current date].

If you use LaTeX, the following can be used instead:

@MISC{SvetunkovAdam,
title = {Forecasting and Analytics with ADAM},
author = {Ivan Svetunkov},
howpublished = {OpenForecast},
note = {(version: [current date])},
year = {2021}
}