@inproceedings{grover2015a,
author = {Grover, Aditya and Kapoor, Ashish and Horvitz, Eric},
title = {A Deep Hybrid Model for Weather Forecasting},
booktitle = {KDD '15 Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
year = {2015},
month = {August},
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.},
publisher = {ACM Press},
url = {http://approjects.co.za/?big=en-us/research/publication/deep-hybrid-model-weather-forecasting/},
pages = {379-386},
isbn = {978-1-4503-3664-2},
edition = {KDD '15 Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
}