SeesawNet dynamically balances common and instance-specific dependencies via ASNA in temporal and channel dimensions, outperforming prior methods on non-stationary forecasting benchmarks.
Are transformers effective for time series fore- casting? InProceedings of the AAAI conference on artifi- cial intelligence, volume 37, pages 11121–11128
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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.
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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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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.