Towards entity-aware conditional variational inference for heterogeneous time-series prediction: An application to hydrology
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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.
Suggested Citation
Ghosh, R., Mcaliley, W., Renganathan, A., Steinbach, M., Duffy, C., Kumar, V., 2024, Towards entity-aware conditional variational inference for heterogeneous time-series prediction: An application to hydrology, in Proceedings of the 2024 SIAM International Conference on Data Mining (SDM), p. 334-342, https://doi.org/10.1137/1.9781611978032.38.
| Publication type | Conference Paper |
|---|---|
| Publication Subtype | Conference Paper |
| Title | Towards entity-aware conditional variational inference for heterogeneous time-series prediction: An application to hydrology |
| DOI | 10.1137/1.9781611978032.38 |
| Publication Date | April 11, 2024 |
| Year Published | 2024 |
| Language | English |
| Publisher | Society for Industrial and Applied Mathematics |
| Contributing office(s) | WMA - Integrated Information Dissemination Division |
| Description | 9 p. |
| Larger Work Type | Conference Paper |
| Larger Work Subtype | Conference Paper |
| Larger Work Title | Proceedings of the 2024 SIAM International Conference on Data Mining (SDM) |
| First page | 334 |
| Last page | 342 |