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.

本文介绍了超树的概念，并提供了将基于树的模型应用于时间序列数据的新方向。与直接预测时间序列的传统决策树应用不同，超树旨在学习目标时间序列模型的参数。我们的框架利用了增强树的基于梯度的性质，使我们能够将超网络的概念扩展到超树，并对树模型引入时间序列归纳偏差。通过将目标时间序列模型的参数与特征相关联，超树解决了参数非平稳性的挑战，并使基于树的预测能够扩展到其初始训练范围之外。通过我们的研究，我们旨在探索超树在各种预测场景中的有效性，以及扩大梯度增强决策树在时间序列预测中的应用。