T1 uses one-to-one channel-head binding in a CNN-Transformer hybrid to achieve robust multivariate time-series imputation, cutting average MSE by 46% versus the next-best baseline across 11 datasets even at 70% missingness.
Wenjie Du, Yiyuan Yang, Linglong Qian, Jun Wang, and Qingsong Wen
3 Pith papers cite this work. Polarity classification is still indexing.
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cs.LG 3years
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HELIX uses learnable feature identities and hybrid temporal-feature attention to achieve state-of-the-art time series imputation across multiple datasets and settings.
PyPOTS is a new open-source toolkit providing unified pipelines for simulation, preprocessing, training, and evaluation on time series with missing data.
citing papers explorer
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T1: One-to-One Channel-Head Binding for Multivariate Time-Series Imputation
T1 uses one-to-one channel-head binding in a CNN-Transformer hybrid to achieve robust multivariate time-series imputation, cutting average MSE by 46% versus the next-best baseline across 11 datasets even at 70% missingness.
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HELIX: Hybrid Encoding with Learnable Identity and Cross-dimensional Synthesis for Time Series Imputation
HELIX uses learnable feature identities and hybrid temporal-feature attention to achieve state-of-the-art time series imputation across multiple datasets and settings.
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End-to-End Learning for Partially-Observed Time Series with PyPOTS
PyPOTS is a new open-source toolkit providing unified pipelines for simulation, preprocessing, training, and evaluation on time series with missing data.