Time series forecasting

This is the repository for the practices of "Time series analysis", a graduate course at FaMAF's #DiploDatos, National University of Córdoba

Based in "A short course on forecasting" by Marcel Scharth, The University of Sydney.

This material draws on the Forecasting Principles and Practice textbook by Rob Hyndman and George Athanasopoulos. While the textbook and Rob Hyndman's DataCamp course use R, our tutorials provide a Python version of related content, but based only in statsmodels and the scipy stack.

This version: September 6, 2019.

Tutorials (Python)

Install requirements using command bellow and then open Jupyter notebook:

conda install --file conda-requirements.txt

docker run -p 8889:8888 gmiretti/forecasting

To update to latest version execute the command below and run again:

docker pull gmiretti/forecasting

Name		Name	Last commit message	Last commit date
Latest commit History 41 Commits
datasets		datasets
.dockerignore		.dockerignore
.gitattributes		.gitattributes
.gitignore		.gitignore
Dockerfile		Dockerfile
Holt adjusting.ipynb		Holt adjusting.ipynb
LSTM.ipynb		LSTM.ipynb
Multivariate Time Series.ipynb		Multivariate Time Series.ipynb
README.md		README.md
Series Temporales.pdf		Series Temporales.pdf
Tutorial01 Time Stamped.ipynb		Tutorial01 Time Stamped.ipynb
Tutorial02 Forecasting.ipynb		Tutorial02 Forecasting.ipynb
Tutorial03 Holt Winters Smoothing.ipynb		Tutorial03 Holt Winters Smoothing.ipynb
Tutorial04 ARIMA.ipynb		Tutorial04 ARIMA.ipynb
Tutorial05 Seasonal ARIMA.ipynb		Tutorial05 Seasonal ARIMA.ipynb
conda-requirements.txt		conda-requirements.txt
get-x13as.sh		get-x13as.sh
plotting.py		plotting.py