  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.
