This project is not exhaustive, but shows how you can get good results quickly by working through a time series forecasting problem systematically.The steps of this project that we will through are as follows.
Time series forecasting is a process, and the only way to get good forecasts is to practice this process.
In this tutorial, you will discover how to forecast the monthly sales of French champagne with Python.
The walk-forward validation will work as follows: Given the small size of the data, we will allow a model to be re-trained given all available data prior to each prediction.
We can write the code for the test harness using simple Num Py and Python code.
This means that we cannot easily collect updated data to validate the model.