Time Series Forecasting with ARIMA
This tutorial demonstrates how to implement the models and forecasting discussed in this unit. Since we are using Google Colab, you can jump to Step 2 to begin this programming example. Upon completing this tutorial, you should be able to construct models, make forecasts and validate forecasts given a time series data set.
Step 1 - Installing Packages
To set up our environment for time-series forecasting, let's first move into our local programming environment or server-based programming environment:
cd environments .my_env/bin/activate
From here, let's create a new directory for our project. We will call it
ARIMA and then move it into the directory. If you call the project a different name, be sure to substitute your name for ARIMA throughout the guidemkdir ARIMA
cd ARIMA
This tutorial will require the
warnings , itertools , pandas , numpy , matplotlib and statsmodels libraries. The warnings and itertools libraries come included with the standard Python library set, so you shouldn't need to install them.Like with other Python packages, we can install these requirements with
pip . We can now install pandas , statsmodels , and the data plotting package matplotlib . Their dependencies will also be installed: pip install pandas numpy statsmodels matplotlib At this point, we're now set up to start working with the installed packages.