Time Series Analysis and Forecasting with ADAM
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.
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,
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. (2020) Time Series Analysis and Forecasting with ADAM: Lancaster, UK. openforecast.org/adam. Accessed on [current date].
If you use LaTeX, the following can be used instead:
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