Name
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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Year | DOI | Venue |
|---|---|---|
2015 | 10.1145/2783258.2783275 | KDD '15: The 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Sydney
NSW
Australia
August, 2015 |
Keywords | Field | DocType |
Gaussian Processes,Deep Learning | Data mining,Multivariate interpolation,Inference,Computer science,Gaussian process,Probabilistic forecasting,Artificial intelligence,Deep learning,Graphical model,Artificial neural network,Weather forecasting,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4503-3664-2 | 23 | 1.44 |
References | Authors | |
10 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aditya Grover | 1 | 1162 | 33.88 |
| Ashish Kapoor | 2 | 1833 | 119.72 |
| Eric Horvitz | 3 | 9402 | 1058.25 |