Forecasting with Hyper-Trees
- URL: http://arxiv.org/abs/2405.07836v2
- Date: Fri, 17 May 2024 14:26:18 GMT
- Title: Forecasting with Hyper-Trees
- Authors: Alexander März, Kashif Rasul,
- Abstract summary: Hyper-Trees are designed to learn the parameters of a target time series model.
By relating the parameters of a target time series model to features, Hyper-Trees address the issue of parameter non-stationarity.
- Score: 50.72190208487953
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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