I am still fairly new to machine learning and to time serie analysis so anything would be appreciated, but what I enjoyed about your article was the fact that you transformed a model that didnt realy have any notion of time into a model that was time aware simply by using the Gaussian Random Walk. Not having a lot of experience with time series, I found the concept really interesting. Also comparing the bayesian way of doing things with the frequentist traditional methods was also very interesting.
There was two things that interested me but where I had no idea where to even start. Hiearchical time series where weights vary by time but also per group (where group weights are partially pooled).
Also multi variate time series where multiples things can affect your response variable. For example you are trying to predict store sales over time and you also have time series of foot traffic in front of the store, weather data, etc.
And thank you for all your great articles on your blog! Really useful for a beginner.