arXiv Machine Learning
Deep Time-series Forecasting Needs Kernelized Moment Balancing
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KReF: Training-Free Retrieval for Long-Term Time-Series Forecasting and Predictive Uncertainty
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GateTS: Versatile and Efficient Forecasting via Attention-Inspired routed Mixture-of-Experts
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Aurora-X: Built for Extreme Time Series Forecasting
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Two-stage Odd Residual Flows for Mean-Preserving Probabilistic Time Series Forecasting
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Selective Time Series Forecasting via Metalearning
Deep learning methods have achieved state-of-the-art in time series forecasting, yet their accuracy varies considerably across samples, as some instances remain inherently difficult to predict. Reject option mechanisms, which allow models to abstain from high-risk predictions, are well established in classification and regression but underexplored in forecasting.
arXiv Machine Learning
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