We introduce the concept of Hyper-Trees and offer 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.
This repo contains the official implementation of our paper Forecasting with Hyper-Trees. The source code of our Hyper-Tree architecture will be made available soon.
💥 [2024-05-13] Added supplementary experiment outputs.
💥 [2024-05-01] Create repository and initial commits.
We encourage you to provide feedback by opening a new discussion.
