Explainable Adaptive Tree-based Model Selection for Time Series
  Forecasting
Matthias Jakobs Amal Saadallah
Tree-based models have been successfully applied to a wide variety of tasks,
including time series forecasting. They are increasingly in demand and widely
accepted because of their comparatively high level of interpretability.
However, many of them suffer from the overfitting problem, which limits their
application in real-world decision-making. This problem becomes even more
severe in online-forecasting settings where time series observations are
incrementally acquired, and the distributions from which they are drawn may
keep changing over time. In this context, we propose a novel method for the
online selection of tree-based models using the TreeSHAP explainability method
in the task of time series forecasting. We start with an arbitrary set of
different tree-based models. Then, we outline a performance-based ranking with
a coherent design to make TreeSHAP able to specialize the tree-based
forecasters across different regions in the input time series. In this
framework, adequate model selection is performed online, adaptively following
drift detection in the time series. In addition, explainability is supported on
three levels, namely online input importance, model selection, and model output
explanation. An extensive empirical study on various real-world datasets
demonstrates that our method achieves excellent or on-par results in comparison
to the state-of-the-art approaches as well as several baselines.