Deep Factors with Gaussian Processes for ForecastingEI

    Danielle C. Maddix
    Danielle C. Maddix
    Cited by: 0|Bibtex|13|

    arXiv: Machine Learning, Volume abs/1812.000982018,

    Abstract:

    A large collection of time series poses significant challenges for classical and neural forecasting approaches. Classical time series models fail to fit data well and to scale to large problems, but succeed at providing uncertainty estimates. The converse is true for deep neural networks. In this paper, we propose a hybrid model that inco...More
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