Temporal Operator Attention augments softmax attention with learnable sequence-space operators for signed temporal mixing and uses stochastic regularization to enable practical training, yielding consistent gains on time series benchmarks.
Duet: Dual clustering enhanced multivariate time series forecasting
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BARFI-Q integrates patch-based embedding, dual-branch temporal modeling, hierarchical fusion, adaptive block-attention residuals, and quantum feature mapping to forecast atom interferometry time-series, outperforming baselines while representing targets in circular sine-cosine space.
Proposes dynamics-based analysis of time series models showing partial dynamics learning and end-positioning as key to performance, plus a plug-and-play improvement method.
citing papers explorer
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Beyond Similarity: Temporal Operator Attention for Time Series Analysis
Temporal Operator Attention augments softmax attention with learnable sequence-space operators for signed temporal mixing and uses stochastic regularization to enable practical training, yielding consistent gains on time series benchmarks.
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BARFI-Q: Quantum-Enhanced Block Attention Residual Fusion Framework for Multivariate Time-Series Forecasting in Atom Interferometry
BARFI-Q integrates patch-based embedding, dual-branch temporal modeling, hierarchical fusion, adaptive block-attention residuals, and quantum feature mapping to forecast atom interferometry time-series, outperforming baselines while representing targets in circular sine-cosine space.
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Time Series Forecasting Through the Lens of Dynamics
Proposes dynamics-based analysis of time series models showing partial dynamics learning and end-positioning as key to performance, plus a plug-and-play improvement method.