mlsanityData to 7 Oct 2026

Mixed moving average field guided learning for spatio-temporal data

arXiv 2301.00736 ↗First posted 2 Jan 2023stat.ML cs.LG math.STcited by 1

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Abstract (arXiv)

Influenced mixed moving average fields are a versatile modeling class for spatio-temporal data. However, their predictive distribution is not generally known. Under this modeling assumption, we define a novel spatio-temporal embedding and a theory-guided machine learning approach that employs a generalized Bayesian algorithm to make ensemble forecasts. We use Lipschitz predictors and determine fixed-time and any-time PAC Bayesian bounds in the batch learning setting. Performing causal forecast is a highlight of our methodology as its potential application to data with spatial and temporal short and long-range dependence. We then test the performance of our learning methodology by using linear predictors and data sets simulated from a spatio-temporal Ornstein-Uhlenbeck process.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 19 of its 72 references that are in the map. First posted on arXiv 2 Jan 2023.

Builds on

19 of 72 references.

References · 19

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  12. 2018Machine Learning for Spatiotemporal Sequence Forecasting: A Surveycited by 19
7 more
  1. 2019PAC-Bayes under potentially heavy tailscited by 16
  2. 2019DisCo: Physics-Based Unsupervised Discovery of Coherent Structures in Spatiotemporal Systemscited by 5
  3. 2015Spatio-temporal Ornstein-Uhlenbeck processes: theory, simulation and statistical inferencecited by 3
  4. 2020Causal Future Prediction in a Minkowski Space-Timecited by 3
  5. 2017Bridging between short-range and long-range dependence with mixed spatio-temporal Ornstein-Uhlenbeck processescited by 2
  6. 2021Inference and forecasting for continuous-time integer-valued trawl processescited by 2
  7. 2015The LICORS Cabinet: Nonparametric Algorithms for Spatio-temporal Predictioncited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Spatio-temporal probabilistic forecast using MMAF-guided learningcited by 0