**Open Review**. I want your feedback to make the book better for you and other readers. To add your annotation, select some text and then click the on the pop-up menu. To see the annotations of others, click the button in the upper right hand corner of the page

# Time Series Analysis and Forecasting with ADAM

*2021-01-24*

# Preface

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. ADAM is a state space model based on exponential smoothing in ETS form and ARIMA. It encompasses both models and is expanded by introducing:

- Explanatory variables (including time varying parameters);
- Multiple frequencies;
- Handling intermittent data (data with natural zeroes);
- Handling missing data;
- Variables and components selection and combination;
- Analysis of parameters of the model;
- And other minor features.

All these extentions 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.

If you want to run exampels from the textbook, two packages are needed (I. Svetunkov 2021a; I. Svetunkov 2021b):

```
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.If you want to get in touch with me, there are lots of ways to do that: comments section on any page of my website, my Russian website, vk.com, Facebook, Linkedin, Twitter.

You can also find me on ResearchGate, StackExchange and StackOverflow, although I’m not really active there. Finally, I also have GitHub account.

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

- Svetunkov, I. (2020) Time Series Analysis and Forecasting with ADAM: Lancaster, UK. openforecast.org/adam. Accessed on [current date].

### License

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.

### References

Svetunkov, Ivan. 2021a. *Greybox: Toolbox for Model Building and Forecasting*. https://github.com/config-i1/greybox.

Svetunkov, Ivan. 2021b. *Smooth: Forecasting Using State Space Models*. https://github.com/config-i1/smooth.