We introduce Hyper-Trees as a novel framework for modeling time series data using gradient boosted trees. Unlike conventional tree-based approaches that forecast time series directly, Hyper-Trees learn the parameters of a target time series model, such as ARIMA or Exponential Smoothing, as functions of features. These parameters are then used by the target model to generate the final forecasts. Our framework combines the effectiveness of decision trees on tabular data with classical forecasting models, thereby inducing a time series inductive bias into tree-based models. To resolve the scaling limitations of boosted trees when estimating a high-dimensional set of target model parameters, we combine decision trees and neural networks within a unified framework. In this hybrid approach, the trees generate informative representations from the input features, which a shallow network then uses as input to learn the parameters of a time series model. With our research, we explore the effectiveness of Hyper-Trees across a range of forecasting tasks and extend tree-based modeling beyond its conventional use in time series analysis.

翻译：我们提出超树作为一种新颖的框架，用于利用梯度提升树建模时间序列数据。与直接预测时间序列的传统树方法不同，超树将目标时间序列模型（如ARIMA或指数平滑）的参数学习为特征的函数。随后，目标模型使用这些参数生成最终预测。我们的框架将决策树在表格数据上的有效性与经典预测模型相结合，从而将时间序列归纳偏置引入基于树的模型。为解决提升树在估计高维目标模型参数集时的扩展性限制，我们在统一框架中结合了决策树与神经网络。在这种混合方法中，树从输入特征生成信息丰富的表示，随后浅层网络将其作为输入来学习时间序列模型的参数。通过本研究，我们探索了超树在一系列预测任务中的有效性，并将基于树的建模扩展到其在时间序列分析中的传统应用范围之外。