From what I get, that is also the advantage of Fugue. From their Webpage:
> FugueSQL is designed for heavy SQL users to extend the boundaries of traditional SQL workflows. FugueSQL allows the expression of logic for end-to-end distributed computing workflows. It can also be combined with Python code to use custom functions alongside the SQL commands. It provides a unified interface, allowing the same SQL code to run on Pandas, Dask, and Spark.

https://github.com/fugue-project/fugue

Imagine this toy example. You have 5 series, which are very sparse, as is often the case in retail. For example, series 1 has sales on Mondays and 0's the rest of the days, series 2 on Tuesdays, series 3 on Wednesdays, and so on. For those individual series, a value close to 0 would be more or less accurate, however, when you add all the predictions up, the value will be way below the true value.

For Hierarchical and sparse data it is quite common to see models achieving good accuracy in the bottom levels but being very bad at higher aggregation levels. This is the case because the models are systematically under or over predicting.

There is no overlap at all. It’s a completely new dataset. There might be similarities in the sense that there are time series or certain frequencies but in no way could it be the talk of “training in the test” set.

How is it biased to try good-performing ensembles in another data set?

And how is that overfitting?

Furthermore, just because the data sets begin with "M" it does not mean that they "have significant overlap and similarity. "

>This ensemble is formed by averaging four statistical models: AutoARIMA, ETS, CES and DynamicOptimizedTheta. This combination won sixth place and was the simplest ensemble among the top 10 performers in the M4 competition.

Here is the repo for hierarchical methods: https://github.com/nixtla/hierarchicalforecast/