SeesawNet dynamically balances common and instance-specific dependencies via ASNA in temporal and channel dimensions, outperforming prior methods on non-stationary forecasting benchmarks.
Re- versible instance normalization for accurate time-series forecasting against distribution shift
3 Pith papers cite this work. Polarity classification is still indexing.
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cs.LG 3years
2026 3representative citing papers
PRISM-VQ integrates vector-quantized latent factors with financial priors and a structure-conditioned mixture-of-experts to deliver improved cross-sectional stock return predictions and portfolio performance on CSI 300 and S&P 500.
NEST improves long-term multivariate forecasting under dataset-level distribution shifts by clustering regimes in moment-entropy space and recomposing specialized variate-attention experts via a content-plus-geometry router.
citing papers explorer
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SeesawNet: Towards Non-stationary Time Series Forecasting with Balanced Modeling of Common and Specific Dependencies
SeesawNet dynamically balances common and instance-specific dependencies via ASNA in temporal and channel dimensions, outperforming prior methods on non-stationary forecasting benchmarks.
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Vector-Quantized Discrete Latent Factors Meet Financial Priors: Dynamic Cross-Sectional Stock Ranking Prediction for Portfolio Construction
PRISM-VQ integrates vector-quantized latent factors with financial priors and a structure-conditioned mixture-of-experts to deliver improved cross-sectional stock return predictions and portfolio performance on CSI 300 and S&P 500.
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NEST: Tackling Dataset-Level Distribution Shifts via Regime-Oriented Mixture-of-Experts
NEST improves long-term multivariate forecasting under dataset-level distribution shifts by clustering regimes in moment-entropy space and recomposing specialized variate-attention experts via a content-plus-geometry router.