MOSAIC recovers identifiable latent variables and their sparse associated observations in scientific time series by combining temporal causal representation learning with support recovery through a sparse additive decoder.
ACM Computing Surveys , volume=
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TTCD uses a non-stationary feature learner and reconstruction-guided distillation inside a transformer to infer contemporaneous and lagged causal graphs from non-stationary time series without strong noise assumptions.
citing papers explorer
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MOSAIC: Module Discovery via Sparse Additive Identifiable Causal Learning for Scientific Time Series
MOSAIC recovers identifiable latent variables and their sparse associated observations in scientific time series by combining temporal causal representation learning with support recovery through a sparse additive decoder.
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TTCD:Transformer Integrated Temporal Causal Discovery from Non-Stationary Time Series Data
TTCD uses a non-stationary feature learner and reconstruction-guided distillation inside a transformer to infer contemporaneous and lagged causal graphs from non-stationary time series without strong noise assumptions.