  This paper introduces the concept of Hyper-Trees and offers a new direction
in applying tree-based models to time series data. Unlike conventional
applications of decision trees that forecast time series directly, Hyper-Trees
are designed to learn the parameters of a target time series model. Our
framework leverages the gradient-based nature of boosted trees, which allows us
to extend the concept of Hyper-Networks to Hyper-Trees and to induce a
time-series inductive bias to tree models. By relating the parameters of a
target time series model to features, Hyper-Trees address the issue of
parameter non-stationarity and enable tree-based forecasts to extend beyond
their training range. With our research, we aim to explore the effectiveness of
Hyper-Trees across various forecasting scenarios and to extend the application
of gradient boosted decision trees outside their conventional use in time
series modeling.
