Deep Factors with Gaussian Processes for Forecasting
This addresses forecasting problems for domains with large-scale time series data, offering a scalable solution with uncertainty handling, though it is incremental as it builds on existing hybrid approaches.
The paper tackles the challenge of forecasting large collections of time series by proposing a hybrid model that combines deep neural networks for scalability and Gaussian processes for uncertainty estimation, achieving higher accuracy than state-of-the-art methods.
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.