Towards Entity-Aware Conditional Variational Inference for Heterogeneous Time-Series Prediction: An application to Hydrology

Rahul Ghosh, Arvind Renganathan, Wallace McAliley, Michael Steinbach, Christopher Duffy, Vipin Kumar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Many environmental systems (e.g., hydrology basins) can be modeled as entity whose response (e.g., streamflow) depends on drivers (e.g., weather) conditioned on their characteristics (e.g., soil properties). We introduce Entity-aware Conditional Variational Inference (EA-CVI), a novel probabilistic inverse modeling approach, to deduce entity characteristics from observed driver-response data. EA-CVI infers probabilistic latent representations that can accurately predict response for diverse entities, particularly in out-of-sample few-shot settings. EA-CVI's latent embeddings encapsulate diverse entity characteristics within compact, low-dimensional representations. EA-CVI proficiently identifies dominant modes of variation in responses and offers the opportunity to infer a physical interpretation of the underlying attributes that shape these responses. EA-CVI can also generate new data samples by sampling from the learned distribution, making it useful in zero-shot scenarios. EA-CVI addresses the need for uncertainty estimation, particularly during extreme events, rendering it essential for data-driven decision-making in real-world applications. Extensive evaluations on a renowned hydrology benchmark dataset, CAMELS-GB, validate EA-CVI's abilities.

Original languageEnglish (US)
Title of host publicationProceedings of the 2024 SIAM International Conference on Data Mining, SDM 2024
EditorsShashi Shekhar, Vagelis Papalexakis, Jing Gao, Zhe Jiang, Matteo Riondato
PublisherSociety for Industrial and Applied Mathematics Publications
Pages334-342
Number of pages9
ISBN (Electronic)9781611978032
StatePublished - 2024
Event2024 SIAM International Conference on Data Mining, SDM 2024 - Houston, United States
Duration: Apr 18 2024Apr 20 2024

Publication series

NameProceedings of the 2024 SIAM International Conference on Data Mining, SDM 2024

Conference

Conference2024 SIAM International Conference on Data Mining, SDM 2024
Country/TerritoryUnited States
CityHouston
Period4/18/244/20/24

Bibliographical note

Publisher Copyright:
Copyright © 2024 by SIAM.

Keywords

  • environmental applications
  • few-shot learning
  • meta-learning
  • representation learning
  • zero-shot learning

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