Warped Input Gaussian Processes for Time Series Forecasting

TL;DR

This paper presents a Gaussian process model with non-parametric input warping to effectively forecast non-stationary time series, handling volatility and change points with high accuracy and low computational cost.

Contribution

It introduces a novel non-parametric warping method for Gaussian processes, improving forecasting in non-stationary time series with efficient training.

Findings

Achieves state-of-the-art forecasting accuracy on synthetic and real data.

Maintains low computational overhead compared to existing methods.

Effectively models non-stationarity including volatility and change points.

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.