PULSE is a physics-informed plug-and-play framework that uses phase-anchored disentanglement, a Phase Router, and statistic-aware mixup to mitigate Phase Amnesia in non-stationary forecasting and achieve strong results with simple backbones.
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4 Pith papers cite this work. Polarity classification is still indexing.
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MDS screens assets using Fréchet variation dependence on weighted point-curve objects of returns and intraday risk, then applies standard allocation, with claimed consistency guarantees and better out-of-sample performance on Chinese high-frequency stock data.
Establishes a uniform Bahadur representation for sieve M-estimators under temporal dependence and constructs valid simultaneous confidence regions using Gaussian approximation and self-convolved bootstrap.
SA-BCP achieves optimal spatio-temporal decoupling in Bayesian conformal prediction by gating temporal inertia with spatial kernel-density evidence, governed by a minimax bias-variance threshold K, and outperforms ACI and Bayesian CP baselines on financial datasets.
citing papers explorer
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PULSE: Generative Phase Evolution for Non-Stationary Time Series Forecasting
PULSE is a physics-informed plug-and-play framework that uses phase-anchored disentanglement, a Phase Router, and statistic-aware mixup to mitigate Phase Amnesia in non-stationary forecasting and achieve strong results with simple backbones.
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Large-Scale Asset Selection via Metric Dependence with Enriched High Frequency Information
MDS screens assets using Fréchet variation dependence on weighted point-curve objects of returns and intraday risk, then applies standard allocation, with claimed consistency guarantees and better out-of-sample performance on Chinese high-frequency stock data.
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Simultaneous Inference for Nonlinear Time Series, a Sieve M-regression Approach
Establishes a uniform Bahadur representation for sieve M-estimators under temporal dependence and constructs valid simultaneous confidence regions using Gaussian approximation and self-convolved bootstrap.
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Optimal Spatio-Temporal Decoupling for Bayesian Conformal Prediction
SA-BCP achieves optimal spatio-temporal decoupling in Bayesian conformal prediction by gating temporal inertia with spatial kernel-density evidence, governed by a minimax bias-variance threshold K, and outperforms ACI and Bayesian CP baselines on financial datasets.