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• W2992115783 abstract "We introduce a Gaussian process-based model for handling of non-stationarity. The warping is achieved non-parametrically, through imposing a prior on the relative change of distance between subsequent observation inputs. The model allows the use of general gradient optimization algorithms for training and incurs only a small computational overhead on training and prediction. The model finds its applications in forecasting in non-stationary time series with either gradually varying volatility, presence of change points, or a combination thereof. We evaluate the model on synthetic and real-world time series data comparing against both baseline and known state-of-the-art approaches and show that the model exhibits state-of-the-art forecasting performance at a lower implementation and computation cost." @default.
• W2992115783 created "2019-12-13" @default.
• W2992115783 creator A5016331473 @default.
• W2992115783 date "2019-12-05" @default.
• W2992115783 modified "2023-09-27" @default.
• W2992115783 title "Warped Input Gaussian Processes for Time Series Forecasting" @default.
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• W2992115783 hasPublicationYear "2019" @default.
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