TimeLAVA values time series segments by marginal contribution to minimizing distributional discrepancy using selective wavelet transforms and unbalanced optimal transport, without model training.
Journal of the American Statistical Association , volume=
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A neural network learns non-stationary anisotropic correlations from gridded CTM outputs and transfers the structure via LatticeKrig basis functions to station data for refined fine-scale NO2 predictions with uncertainty.
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TimeLAVA: Learning-Agnostic Valuation for Time Series Data
TimeLAVA values time series segments by marginal contribution to minimizing distributional discrepancy using selective wavelet transforms and unbalanced optimal transport, without model training.
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A Non-stationary, Amortized, Transfer Learning Approach for Modeling Italian Air Quality
A neural network learns non-stationary anisotropic correlations from gridded CTM outputs and transfers the structure via LatticeKrig basis functions to station data for refined fine-scale NO2 predictions with uncertainty.