These plots give us a little insight into how the model is formed. The trend plot (top) exhibits a linear, piecewise function, with approximately appropriate values for our dataset throughout the years. This looks to be a baseline for predictions.
The weekly plot (middle) demonstrates some interesting behaviour – weekdays have a small negative impact on the predictions (approximately -50), and we see large spikes for the weekends. This appears peculiar, as we have no weekend data in our dataset, but it is a product of fitting a 7-day periodic function to only 5 days of data. Thankfully, this won’t be an issue as we have no need to forecast weekends.
The yearly plot (bottom) shows a much more volatile impact on predictions (-200 to +180) with frequent changepoints throughout. This points to a more sensitive and complex relationship between the time of year and the FTSE100 index than the day of the week.
If you’re already familiar with techniques like ARMA or ARIMA, this post will let you see immediately what the key differences are.