%0 Conference Paper %1 Grover:2015:DHM:2783258.2783275 %A Grover, Aditya %A Kapoor, Ashish %A Horvitz, Eric %B Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining %C New York, NY, USA %D 2015 %I ACM %K deep_learning environment neural_network %P 379--386 %R 10.1145/2783258.2783275 %T A Deep Hybrid Model for Weather Forecasting %U http://doi.acm.org/10.1145/2783258.2783275 %X 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. %@ 978-1-4503-3664-2

@inproceedings{Grover:2015:DHM:2783258.2783275, 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.}, acmid = {2783275}, added-at = {2016-09-29T15:22:19.000+0200}, address = {New York, NY, USA}, author = {Grover, Aditya and Kapoor, Ashish and Horvitz, Eric}, biburl = {https://www.bibsonomy.org/bibtex/2244b3d0ea886d75ec5db548d3aa2db59/dallmann}, booktitle = {Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining}, doi = {10.1145/2783258.2783275}, interhash = {46bc7aa2bf55beeb0b597815054a6434}, intrahash = {244b3d0ea886d75ec5db548d3aa2db59}, isbn = {978-1-4503-3664-2}, keywords = {deep_learning environment neural_network}, location = {Sydney, NSW, Australia}, numpages = {8}, pages = {379--386}, publisher = {ACM}, series = {KDD '15}, timestamp = {2016-09-29T15:22:19.000+0200}, title = {A Deep Hybrid Model for Weather Forecasting}, url = {http://doi.acm.org/10.1145/2783258.2783275}, year = 2015 }