# The LICORS Cabinet: Nonparametric Algorithms for Spatio-temporal Prediction

arXiv: https://arxiv.org/abs/1506.02686 · HTML: https://mlsanity.com/paper/1506.02686/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2015.

## Built on by (most cited first, 1 of 1)

- [Mixed moving average field guided learning for spatio-temporal data](https://mlsanity.com/paper/2301.00736/) (2023)

## Abstract (arXiv)

Spatio-temporal data is intrinsically high dimensional, so unsupervised modeling is only feasible if we can exploit structure in the process. When the dynamics are local in both space and time, this structure can be exploited by splitting the global field into many lower-dimensional "light cones". We review light cone decompositions for predictive state reconstruction, introducing three simple light cone algorithms. These methods allow for tractable inference of spatio-temporal data, such as full-frame video. The algorithms make few assumptions on the underlying process yet have good predictive performance and can provide distributions over spatio-temporal data, enabling sophisticated probabilistic inference.