[wenc]

> Nothing reliably consistently beats ARIMA models in time series forecasting to this day

Not sure this is true in practice. In some situations, Holt-Winters (ie. algorithms in the ETS family) may do better, and it's often a good idea to try both.

There's a claim that Holt-Winters is a special case of ARIMA (the claim is ARIMA is more general), but this is actually not the case. There is equivalence in only a subset of cases. [1]

I've fitted Holt-Winters models that beat ARIMA models. ARIMA models can have trouble generalizing from training data with long horizons because they tend to overfit to the distant past. Holt-Winters on the other hand has a natural "forgetting factor" built-in which moderates this.

As well, my experience is that stacked models with well-chosen exogeneous variables (if you have causal variables) tend to outperform pure time-series methods because they are anchored on more independent variables than just t. Pure time-series models bank on the assumption that patterns have a repeatable time-dependence, and most of the time this is just not true, so have to be augmented with other variables.

[1] https://otexts.com/fpp2/arima-ets.html