Here is another fascinating aspect of the seasonal profile in your data: it can evolve over time due to changing consumer preferences. How so? Let me explain.
I’ve worked with a couple of companies where there were some examples of data with drastically changing seasonal patterns over just a few years. For example, Before Covid (BC), the consumer behaviour could be different than After the Disruption (AD): people started ordering online some products that they used to buy in shops. Some practitioners told me that the seasonal patterns in their data had changed so dramatically that historical data BC had become practically useless.
Why is this important?
If you don’t recognise that the seasonal patterns can change and simply use seasonal dummy variables (for example, in regression or decision trees), you’ll run into problems, as these approaches won’t capture the evolving profile. The same applies to classical decomposition (see Section 3.2 of ADAM), since it assumes a fixed seasonal structure. In fact, any model that assumes fixed seasonality would fail in this situation, and you may not even notice (see image in the post, where the model fails to capture the profile correctly because it assumes that it is fixed).
What can we do with that?
The solution is to use approaches that allow seasonality to evolve over time. ARIMA and ETS handle this via their parameters, while STL decomposition produces a dynamic seasonal profile. In regression or decision trees, you could incorporate lagged sales to partially account for this, or bring in the seasonal component from STL/ETS as an additional feature.
All good? Not quite, because there is a nasty small grey elephant hidden in this room.
Even if your chosen method allows seasonality to evolve, you must ensure that forecasting uncertainty reflects this properly. If the seasonal pattern in your training set changed drastically, what prevents it from shifting again in the test set? This is particularly critical if you need predictive distributions, such as for setting safety stock levels or generating prediction intervals. Ignoring the fact that the seasonality might change further could make your predictive distribution narrower than expected, leading to potential lost sales.
The good news: ARIMA and ETS handle this naturally, as components’ uncertainty translates directly to the holdout variance. In ML, it is more complicated, because you would need to invest time in proper feature engineering to explicitly capture the potential seasonality changes. Unfortunately, I haven’t done much in the latter direction, so I cannot give you a good recipe. Any thoughts what to do here?
And what do you do in the situation like that?