Applied Time Series Analysis and Forecasting with R
As the name implies, the book focuses on applied data science methods for time series analysis and forecasting, covering (see the full table of content below):
- Working with time-series data
- Time series analysis methods
- Forecasting methods
- Scaling and productionize approaches
Get updates on the book’s progress on Twitter, Telegram channel, and Github project tracker:
This repository hosts the book materials. It follows the [Monorepo](https://en.wikipedia.org/wiki/Monorepo) philosophy, hosting all the book's content, code, packages, and other supporting materials under one repository. In addition, to ensure a high level of reproducibility, the book is developed in a dockerized environment.
Here is the current repository folder structure:
``` shell
.
├── R
├── docker
└── docs
```
- The `R` folder contains the book's supporting R packages
- The `docker` folder provides the build files for the book Docker image
- The `docs` folder hosts the book website files
### Roadmap
Below is the book roadmap:
* `V1` - Foundation of time series analysis
* `V2` - Traditional time series forecasting methods (Smoothing, ARIMA, Linear Regression)
* `V3` - Advanced regression methods (GLM, GAM, etc.)
* `V4` - Bayesian forecasting approaches
* `V5` - Machine and deep learning methods
* `V6` - Scaling and production approaches
### Docker
While it is not required, the book is built with Docker to ensure a high level of reproducibility.
### Table of Contents
- [ ] Preface (V1)
- [ ] Introduction (V1)
- [ ] Prerequisites (V1)
- [ ] Dates and Times Objects (V1)
- [ ] The ts Class (V1)
- [ ] The timetk Class (V1)
- [ ] The tsibble Class (V1)
- [ ] Working with APIs (V2)
- [ ] Plotting Time Series Objects (V1)
- [ ] Seasonal Analysis (V1)
- [ ] Correlation Analysis (V1)
- [ ] Cluster Analysis (V2)
- [ ] Smoothing Methods (V1)
- [ ] Time Series Decomposition (V1)
- [ ] Forecasting Strategies (V2)
- [ ] Forecasting with Smoothing Models (V2)
- [ ] Time Series Properties (V2)
- [ ] Forecasting with ARIMA Models (V2)
- [ ] Forecasting with Linear Regression Model (V2)
- [ ] Forecasting with GLM Model (V3)
- [ ] Forecasting with GAM Model (V3)
- [ ] Forecasting with Bayesian Methods (V4)
- [ ] Forecasting with Machine Learning Methods (V5)
- [ ] Forecasting with Deep Learning Methods (V5)
- [ ] Forecasting at Scale (V6)
- [ ] Forecasting in Production (V6)
## License
This book is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/) License.