# mlr3spatiotempcv: Spatiotemporal resampling methods for machine learning in R

arXiv: https://arxiv.org/abs/2110.12674 · HTML: https://mlsanity.com/paper/2110.12674/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 86 references that are in the map.
- First posted on arXiv 25 Oct 2021.

## Builds on (most cited first, 3 of 3)

- [ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R](https://mlsanity.com/paper/1508.04409/) (2015)
- [Predicting into unknown space? Estimating the area of applicability of spatial prediction models](https://mlsanity.com/paper/2005.07939/) (2020)
- [Estimating the Prediction Performance of Spatial Models via Spatial k-Fold Cross Validation](https://mlsanity.com/paper/2005.14263/) (2020)

## Built on by (most cited first, 4 of 4)

- [Spatial machine-learning model diagnostics: a model-agnostic distance-based approach](https://mlsanity.com/paper/2111.08478/) (2021)
- [Evaluating machine learning models in non-standard settings: An overview and new findings](https://mlsanity.com/paper/2310.15108/) (2023)
- [Gradient Boosting for Spatial Panel Models with Random and Fixed Effects](https://mlsanity.com/paper/2603.14543/) (2026)
- [Aligning Validation with Deployment in Spatial Prediction: Target-Weighted Cross-Validation](https://mlsanity.com/paper/2603.29981/) (2026)

## Abstract (arXiv)

Spatial and spatiotemporal machine-learning models require a suitable framework for their model assessment, model selection, and hyperparameter tuning, in order to avoid error estimation bias and over-fitting. This contribution reviews the state-of-the-art in spatial and spatiotemporal cross-validation, and introduces the {R} package {mlr3spatiotempcv} as an extension package of the machine-learning framework {mlr3}. Currently various {R} packages implementing different spatiotemporal partitioning strategies exist: {blockCV}, {CAST}, {skmeans} and {sperrorest}. The goal of {mlr3spatiotempcv} is to gather the available spatiotemporal resampling methods in {R} and make them available to users through a simple and common interface. This is made possible by integrating the package directly into the {mlr3} machine-learning framework, which already has support for generic non-spatiotemporal resampling methods such as random partitioning. One advantage is the use of a consistent nomenclature in an overarching machine-learning toolkit instead of a varying package-specific syntax, making it easier for users to choose from a variety of spatiotemporal resampling methods. This package avoids giving recommendations which method to use in practice as this decision depends on the predictive task at hand, the autocorrelation within the data, and the spatial structure of the sampling design or geographic objects being studied.