A learnable fusor that reads meta-features of an input time series and weights 13 pre-trained forecasters per sample outperforms each individual model on most benchmark samples, including zero-shot settings.
IMBENS: Ensemble Class-imbalanced Learning in Python
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abstract
imbalanced-ensemble, abbreviated as imbens, is an open-source Python toolbox for leveraging the power of ensemble learning to address the class imbalance problem. It provides standard implementations of popular ensemble imbalanced learning (EIL) methods with extended features and utility functions. These ensemble methods include resampling-based, e.g., under/over-sampling, and reweighting-based, e.g., cost-sensitive learning. Beyond the implementation, we empower EIL algorithms with new functionalities like customizable resampling scheduler and verbose logging, thus enabling more flexible training and evaluating strategies. The package was developed under a simple, well-documented API design that follows scikit-learn for increased ease of use. imbens is released under the MIT open-source license and can be installed from Python Package Index (PyPI) or https://github.com/ZhiningLiu1998/imbalanced-ensemble.
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Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting
A learnable fusor that reads meta-features of an input time series and weights 13 pre-trained forecasters per sample outperforms each individual model on most benchmark samples, including zero-shot settings.