Deep Factors with Gaussian Processes for Forecasting
arXiv:1812.00098
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 incorporates the benefits of both approaches. Our new method is data-driven and scalable via a latent, global, deep component. It also handles uncertainty through a local classical Gaussian Process model. Our experiments demonstrate that our method obtains higher accuracy than state-of-the-art methods.
arXiv commentThird workshop on Bayesian Deep Learning (NeurIPS 2018), Montreal, Canada
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- Causal Inference for Time series Analysis: Problems, Methods and Evaluation
- MixSeq: Connecting Macroscopic Time Series Forecasting with Microscopic Time Series Data
- You May Not Need Order in Time Series Forecasting
- Graph Deep Factors for Forecasting