Factor-augmented tree ensembles. (arXiv:2111.14000v2 [stat.ML] UPDATED)

This manuscript proposes to extend the information set of time-series
regression trees with latent stationary factors extracted via state-space
methods. In doing so, this approach generalises time-series regression trees on
two dimensions. First, it allows to handle predictors that exhibit measurement
error, non-stationary trends, seasonality and/or irregularities such as missing
observations. Second, it gives a transparent way for using domain-specific
theory to inform time-series regression trees. As a byproduct, this technique
sets the foundations for structuring powerful ensembles. Their real-world …

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