samithaj / atsaf

Applied Time Series Analysis and Forecasting

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

This repository hosts the book materials. It follows the 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:

.
├── 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 License.