Time Series Handbook

This handbook extensively covers time series analysis and forecasting, delving from the most fundamental methods to the state-of-the-art. The handbook is designed such that readers can both learn the theory and apply them to real-world problems. Although chapters were made to be stand alone, it is recommended that readers go through the first few chapters to be able to fully appreciate the latter chapters.

The handbook is structured as follows: in the first part, classical forecasting methods are discussed in detail. The subsequent part is then dedicated to dynamical forecasting methods and as well as causality and correlations, topics that are particularly essential in understanding the intricacies of time series forecasting. Finally, the last chapter shows a glimpse into the current trends in time series forecasting and modeling.

Outline

This handbook contains a variety of techniques that you can use for time series analysis – from simple statistical models to some of the state-of-the-art algorithms as of writing. Here are the items that are covered in this material: - Preface: Advanced Time Series Analysis - Chapter 1: Autoregressive integrated moving average - Chapter 2: Linear Trend and Momentum Forecasting - Chapter 3: Vector Autoregressive Methods - Chapter 4: Granger Causality - Chapter 5: Simplex and S-map Projections - Chapter 6: Convergent Cross Mapping and Sugihara Causality - Chapter 7: Cross-Correlations, Fourier Transform and Wavelet Transform - Chapter 8: Winningest Methods

How to use this reference

Each of the chapters mentioned above includes Jupyter notebook/s that contain/s the discussion of each topic (background, limitations, applications). Most of the datasets used in the handbook are included in this repository, and the details of each are described in the data folder.

Setting up your virtual environment

To be able to run the contents of this repository, it is advised that you setup a virtual environment. You can install one via Anaconda or via Python’s native venv module.

Anaconda

To set up a virtual environment called atsa, run the following in your terminal:

# this will create an anaconda environment
# called atsa in 'path/to/anaconda3/envs/'
conda create -n atsa

To activate and enter the environment, run conda activate atsa. To deactivate the environment, either run conda deactivate atsa or exit the terminal. For more information on setting up your virtual evironment using Anaconda, please visit this page.

Python venv

To set up a virtual environment called atsa, run the following in your terminal:

# this will create a virtual environment
# called atsa in your home directory
python3 -m venv ~/atsa

To activate and enter the environment, run source ~/atsa/bin/activate. To deactivate the environment, either run deactivate or exit the terminal. Note that every time you want to work on the assignment, you should rerun source ~/atsa/bin/activate.

Rendering the notebooks

To view the individual notebooks outside of Github without setting up a repository or installing any software, you may use The Jupyter Notebook Viewer.
- Open https://nbviewer.jupyter.org/ - Paste the link to the notebook.

When a notebook rendered in nbviewer appears differently from the one rendered github, just append ?flush_cache=true to the end of the nbviewer URL to force it to rerender.

Jupyterbook

To view all the chapters of this handbook, please visit this link: Time Series Analysis Handbook

Contributors

• Benjur Emmanuel Borja
• Gilbert Michael G. Chua
• Francis James Corpuz
• Carlo Vincienzo Dajac
• Sebastian C. Ibañez
• Prince Joseph Erneszer Javier
• Marissa P. Liponhay
• Maria Eloisa M. Ventura