( Image credit: Alexandra M. Schmidt )
Sparsity and missing data problems are very common in spatiotemporal traffic data collected from various sensing systems.
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.
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.
Multiple imputation by chained equations (MICE) is a flexible and practical approach to handling missing data.
Accordingly, we call our method Generative Adversarial Imputation Nets (GAIN).
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.