AeroSense directly models microscopic aircraft states with masked self-attention to predict heterogeneous air traffic flows, outperforming time series baselines on real airport data.
Phaseformer: From patches to phases for efficient and effective time series forecasting
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
FRWKV+ improves frequency-space linear time series forecasting by exchanging compact contexts between real/imaginary streams and adaptively admitting periodic-position corrections via trust-gated signed adjustments.
citing papers explorer
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From Time Series to State: Situation-Aware Modeling for Air Traffic Flow Prediction
AeroSense directly models microscopic aircraft states with masked self-attention to predict heterogeneous air traffic flows, outperforming time series baselines on real airport data.
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FRWKV+: Adaptive Periodic-Position Branch Interaction for Frequency-Space Linear Time Series Forecasting
FRWKV+ improves frequency-space linear time series forecasting by exchanging compact contexts between real/imaginary streams and adaptively admitting periodic-position corrections via trust-gated signed adjustments.