LeapTS reformulates forecasting as adaptive multi-horizon scheduling via hierarchical control and NCDEs, delivering at least 7.4% better performance and 2.6-5.3x faster inference than Transformer baselines while adapting to non-stationary dynamics.
International journal of forecasting , volume=
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PPM injects parametric structural priors into generative models via a learnable mapping to improve probabilistic forecasts on non-stationary MTS data.
GCSVR combines graph convolutions for spatial station dependencies with SVR for nonlinear temporal patterns, yielding more accurate and stable air pollution forecasts on Delhi and Mumbai datasets than standard benchmarks.
citing papers explorer
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LeapTS: Rethinking Time Series Forecasting as Adaptive Multi-Horizon Scheduling
LeapTS reformulates forecasting as adaptive multi-horizon scheduling via hierarchical control and NCDEs, delivering at least 7.4% better performance and 2.6-5.3x faster inference than Transformer baselines while adapting to non-stationary dynamics.
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Parametric Prior Mapping Framework for Non-stationary Probabilistic Time Series Forecasting
PPM injects parametric structural priors into generative models via a learnable mapping to improve probabilistic forecasts on non-stationary MTS data.
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Graph Convolutional Support Vector Regression for Robust Spatiotemporal Forecasting of Urban Air Pollution
GCSVR combines graph convolutions for spatial station dependencies with SVR for nonlinear temporal patterns, yielding more accurate and stable air pollution forecasts on Delhi and Mumbai datasets than standard benchmarks.