( Image credit: Alexandra M. Schmidt )
In this paper, we propose a Bayesian temporal factorization (BTF) framework for modeling multidimensional time series---in particular spatiotemporal data---in the presence of missing values.
Building upon domain adaptation work, we propose gimVI, a deep generative model for the integration of spatial transcriptomic data and scRNA-seq data that can be used to impute missing genes.
Sparsity and missing data problems are very common in spatiotemporal traffic data collected from various sensing systems.
Multiple imputation by chained equations (MICE) is a flexible and practical approach to handling missing data.
#4 best model for Multivariate Time Series Imputation on KDD CUP Challenge 2018
In this work, we introduce a general probabilistic model that describes sparse high dimensional imaging data as being generated by a deep non-linear embedding.
Accordingly, we call our method Generative Adversarial Imputation Nets (GAIN).
#3 best model for Multivariate Time Series Imputation on KDD CUP Challenge 2018
This paper takes a step towards temporal reasoning in a dynamically changing video, not in the pixel space that constitutes its frames, but in a latent space that describes the non-linear dynamics of the objects in its world.
The imputeTS package specializes on univariate time series imputation.
#2 best model for Multivariate Time Series Imputation on PhysioNet Challenge 2012