Myles Mitchell builds an ARIMA model:
In time series analysis we are interested in sequential data made up of a series of observations taken at regular intervals. Examples include:
- Weekly hospital occupancy
- Monthly sales figures
- Annual global temperature
In many cases we want to use the observations up to the present day to predict (or forecast) the next N time points. For example, a hospital could reduce running costs if an appropriate number of beds are provisioned.
Read on for a primer on the topic, a quick explanation of ARIMA, and a sample implementation using several Python packages.