The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
A Deep Hybrid Model for Weather Forecasting
Abstract
Weather forecasting is a canonical predictive challenge that has depended primarily on model-based methods. We explore new directions with forecasting weather as a data-intensive challenge that involves inferences across space and time. We study specifically the power of making predictions via a hybrid approach that combines discriminatively trained predictive models with a deep neural network that models the joint statistics of a set of weather-related variables. We show how the base model can be enhanced with spatial interpolation that uses learned long-range spatial dependencies. We also derive an efficient learning and inference procedure that allows for large scale optimization of the model parameters. We evaluate the methods with experiments on real-world meteorological data that highlight the promise of the approach.
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Cites
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LSTM-based traffic flow prediction with missing data
Tian, Y., Zhang, K., Li, J., Lin, X., & Yang, B. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.08.067 · Cited by 544
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Improving Subseasonal Forecasting in the Western U.S. with Machine Learning
Hwang, J., Orenstein, P., Cohen, J., Pfeiffer, K., & Mackey, L. (2019) · Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3292500.3330674 · Cited by 85
Small perturbations are enough: Adversarial attacks on time series prediction
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Metadata
- Authors
- Grover, A., Kapoor, A., & Horvitz, E.
- Venue
- Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
- Publisher
- ACM
- Published
- 2015-08-10
- Pages
- 379-386
- OpenAlex
- W2002521620
- Cited by
- 276
- References
- 17
- FWCI
- 226.12
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Grover, A., Kapoor, A., & Horvitz, E. (2015). A Deep Hybrid Model for Weather Forecasting. Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. https://doi.org/10.1145/2783258.2783275
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
