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

Subject Headings:

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

Quotes

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
2015 ADeepHybridModelforWeatherForec	Eric Horvitz Ashish Kapoor Aditya Grover			A Deep Hybrid Model for Weather Forecasting				10.1145/2783258.2783275		2015