2015 ADeepHybridModelforWeatherForec
- (Grover et al., 2015) ⇒ Aditya Grover, Ashish Kapoor, and Eric Horvitz. (2015). “A Deep Hybrid Model for Weather Forecasting.” In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2015). ISBN:978-1-4503-3664-2 doi:10.1145/2783258.2783275
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Notes
Cited By
- http://scholar.google.com/scholar?q=%222015%22+A+Deep+Hybrid+Model+for+Weather+Forecasting
- http://dl.acm.org/citation.cfm?id=2783258.2783275&preflayout=flat#citedby
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Author Keywords
- Deep learning; gaussian processes; graphical models; learning; machine learning; 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 dependency|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.
References
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Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
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2015 ADeepHybridModelforWeatherForec | Eric Horvitz Ashish Kapoor Aditya Grover | A Deep Hybrid Model for Weather Forecasting | 10.1145/2783258.2783275 | 2015 |