What is ADAM?

ADAM stands for “Augmented Dynamic Adaptive Model.” The term “adaptive model” means that the parameters of the model change over time according to some assumed process. The word “dynamic” reflects the idea that the model has time series related components (ETS, ARIMA). Finally, the word “augmented” is included because ADAM is the model that supports additional features not included in the conventional ETS / ARIMA. ADAM is a unified framework for constructing ETS / ARIMA / regression, based on more advanced statistical instruments. For example, classical ARIMA is built on the assumption of normality of the error term, but ADAM lifts this assumption and allows using other distributions as well (e.g. Generalised Normal, Inverse Gaussian etc). Another example, typically the conventional models are estimated either via the maximisation of the likelihood function or using basic losses like MSE or MAE, but ADAM has a broader spectrum of losses and allows using custom ones. There is much more, and we will discuss different aspects of ADAM in detail later in this textbook. The ADAM model includes:

  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 to solve specific real life problems, so we will have examples and case studies later in the book 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 the 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 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 (Svetunkov, 2022, 2021a):


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

An 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. I assume 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. (2022) Forecasting and Analytics with ADAM: Lancaster, UK. Accessed on [current date].

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

    title = {Forecasting and Analytics with ADAM},
    author = {Ivan Svetunkov},
    howpublished = {OpenForecast},
    note = {(version: [current date])},
    url = {},
    year = {2022}


This textbook is licensed under Creative Common License by-nc-sa 4.0, which means that you can share, copy, redistribute and remix the content of the textbook for non-commercial purposes as long as you give appropriate credit to the author and provide the link to the original license. If you remix, transform, or build upon the material, you must distribute your contributions under the same CC-BY-NC-SA 4.0 license. See the explanation on the Creative Commons website.


I would like to thank Tobias Schmidt for his help in refining earlier parts of the textbook and correcting grammatical mistakes.


• Svetunkov, I., 2022. Greybox: Toolbox for model building and forecasting. R package version 1.0.3
• Svetunkov, I., 2021a. Smooth: Forecasting using state space models. R package version