Forecasting with Hyper-Trees

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 challenge of parameter non-stationarity and enable tree-based forecasts to extend beyond their initial training range. With our research, we aim to explore the effectiveness of Hyper-Trees across various forecasting scenarios and to expand the application of gradient boosted decision trees past their conventional use in time series forecasting.