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
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
fields
cs.LG 4years
2026 4verdicts
UNVERDICTED 4representative citing papers
FRWKV-Plus augments the FRWKV backbone with a cross-branch spectral gate and trust-gated residual correction to refine periodic handling in frequency-domain forecasting while remaining lightweight.
Zeus proposes a multi-scale Transformer with point-wise tokenization and Multi-Objective Temporal Masking to enable tuning-free performance on forecasting, interpolation, and other time series tasks.
UPLOTS proposes a unified prompt-guided pretrained transformer for generating constrained time-series data across diverse domains using dynamic multi-dataset loss re-weighting.
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+: Periodic-Aware Adaptive Gating for Frequency-Space Linear Time Series Forecasting
FRWKV-Plus augments the FRWKV backbone with a cross-branch spectral gate and trust-gated residual correction to refine periodic handling in frequency-domain forecasting while remaining lightweight.
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Zeus: Towards Tuning-Free Foundation Model for Time Series Analysis
Zeus proposes a multi-scale Transformer with point-wise tokenization and Multi-Objective Temporal Masking to enable tuning-free performance on forecasting, interpolation, and other time series tasks.
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UPLOTS: A Unified Pretrained Language Model for Constrained Time-series Generation
UPLOTS proposes a unified prompt-guided pretrained transformer for generating constrained time-series data across diverse domains using dynamic multi-dataset loss re-weighting.