Educational matherials for the Course CS-GH301.
An Introduction to Time Series Forecasting with Python
- Dr. Andrii Gakhov
- https://www.gakhov.com
Course Abstract
In this course, we learn how to analyze and forecast time series, study the basic theoretical concepts without going too deep into mathematical aspects, examine different models and techniques. Along the way, we make our hands dirty applying all studied models to a real-world dataset of UK foreign visits using such trendy Python libraries as StatsModels, Prophet, scikit-learn, and keras.
You will see, there is nothing complex in understanding and forecasting time series, you just need the right tools and the knowledge.
Repository
Datasets
Dependencies
- Python 3.4, 3.5, 3.6 (http://python.org/download/)
Install with pip
Installation requires a working build environment that can be build automatically using make utility:
$ make
$ make run
After these commands your default browser should open a Jupyter notebook's index page.
Source code
Author
Andrii Gakhov is a mathematician and software engineer holding a Ph.D. in mathematical modeling and numerical methods. He has been a teacher for a number of years in the School of Computer Science at V. Karazin Kharkov National University, Ukraine and currently works as a software practitioner for ferret go GmbH, the leading community moderation, automation, and analytics company in Germany.
- Andrii Gakhov andrii.gakhov@gmail.com