Statistical Deep Learning for Spatial and Spatio-Temporal Data
- URL: http://arxiv.org/abs/2206.02218v1
- Date: Sun, 5 Jun 2022 16:49:10 GMT
- Title: Statistical Deep Learning for Spatial and Spatio-Temporal Data
- Authors: Christopher K. Wikle and Andrew Zammit-Mangion
- Abstract summary: We present an overview of traditional statistical and machine learning perspectives for modeling spatial andtemporal data.
We then focus on a variety of hybrid models that have recently been developed for latent process, data, and parameter specifications.
These hybrid models integrate modeling ideas with deep neural network models in order to take advantage of the strengths of each modeling paradigm.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Deep neural network models have become ubiquitous in recent years, and have
been applied to nearly all areas of science, engineering, and industry. These
models are particularly useful for data that have strong dependencies in space
(e.g., images) and time (e.g., sequences). Indeed, deep models have also been
extensively used by the statistical community to model spatial and
spatio-temporal data through, for example, the use of multi-level Bayesian
hierarchical models and deep Gaussian processes. In this review, we first
present an overview of traditional statistical and machine learning
perspectives for modeling spatial and spatio-temporal data, and then focus on a
variety of hybrid models that have recently been developed for latent process,
data, and parameter specifications. These hybrid models integrate statistical
modeling ideas with deep neural network models in order to take advantage of
the strengths of each modeling paradigm. We conclude by giving an overview of
computational technologies that have proven useful for these hybrid models, and
with a brief discussion on future research directions.
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