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Nonparametric Mixture of Sparse Regressions on Spatio-Temporal Data -- An Application to Climate Prediction

Authors:

Auroop Ganguly,

Jennifer DyAuthors Info & Claims

KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Pages 2556 - 2564

https://doi.org/10.1145/3292500.3330692

Published: 25 July 2019 Publication History

Abstract

Climate prediction is a very challenging problem. Many institutes around the world try to predict climate variables by building climate models called General Circulation Models (GCMs), which are based on mathematical equations that describe the physical processes. The prediction abilities of different GCMs may vary dramatically across different regions and time. Motivated by the need of identifying which GCMs are more useful for a particular region and time, we introduce a clustering model combining Dirichlet Process (DP) mixture of sparse linear regression with Markov Random Fields (MRFs). This model incorporates DP to automatically determine the number of clusters, imposes MRF constraints to guarantee spatio-temporal smoothness, and selects a subset of GCMs that are useful for prediction within each spatio-temporal cluster with a spike-and-slab prior. We derive an effective Gibbs sampling method for this model. Experimental results are provided for both synthetic and real-world climate data.

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Cited By

Liu YDuffy KDy JGanguly A(2023)Explainable deep learning for insights in El Niño and river flowsNature Communications10.1038/s41467-023-35968-514:1Online publication date: 20-Jan-2023
https://doi.org/10.1038/s41467-023-35968-5
Huang YTang YZhu XZhuang HCherubin L(2022)Physics-Coupled Spatio-Temporal Active Learning for Dynamical SystemsIEEE Access10.1109/ACCESS.2022.321454410(112909-112920)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3214544

Index Terms

Nonparametric Mixture of Sparse Regressions on Spatio-Temporal Data -- An Application to Climate Prediction
1. Applied computing
  1. Physical sciences and engineering
    1. Earth and atmospheric sciences
      1. Environmental sciences
2. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Mixture modeling

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Published In

cover image ACM Conferences

KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

July 2019

3305 pages

ISBN:9781450362016

DOI:10.1145/3292500

General Chairs:
Ankur Teredesai
KenSci
,
Vipin Kumar
University of Minnesota
,
Program Chairs:
Ying Li
EV Analysis Corporation
,
Rómer Rosales
LinkedIn
,
Evimaria Terzi
Boston University
,
George Karypis
University of Minnesota

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 25 July 2019

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National Science Foundation

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KDD '19

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KDD '19: The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 4 - 8, 2019

AK, Anchorage, USA

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KDD '19 Paper Acceptance Rate 110 of 1,200 submissions, 9%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Liu YDuffy KDy JGanguly A(2023)Explainable deep learning for insights in El Niño and river flowsNature Communications10.1038/s41467-023-35968-514:1Online publication date: 20-Jan-2023
https://doi.org/10.1038/s41467-023-35968-5
Huang YTang YZhu XZhuang HCherubin L(2022)Physics-Coupled Spatio-Temporal Active Learning for Dynamical SystemsIEEE Access10.1109/ACCESS.2022.321454410(112909-112920)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3214544

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