arXiv:2405.07836v5 Announce Type: replace Abstract: 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
This development emerges as the AI field seeks more robust and interpretable methods for time series forecasting, combining the strengths of machine learning with established statistical models.
A strategic reader should care because improving time series forecasting has broad applications across finance, logistics, energy, and scientific research, potentially leading to more efficient resource allocation and predictive analytics.
This framework changes how AI models integrate with traditional forecasting methods, moving beyond direct prediction to learning model parameters, offering a more nuanced and potentially more accurate approach.
- · Machine Learning Researchers
- · Time Series Analysts
- · Financial Institutions
- · Logistics and Supply Chain Companies
- · Generic Black-Box Forecasting Models
- · Purely Statistical Forecasting Methods
Improved accuracy and interpretability in critical forecasting applications.
Reduced operational costs and better strategic planning for industries reliant on time series predictions.
New hybrid AI model architectures that flexibly combine interpretability with predictive power across various data types.
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Read at arXiv cs.LG