Dependent time changed processes with applications to nonlinear ocean waves

TL;DR

This paper introduces a novel dependent time change model for Gaussian processes that captures asymmetries in environmental data, with applications to nonlinear ocean waves, providing explicit stationarity and ergodicity properties.

Contribution

It proposes a new trajectory-dependent time change model for Gaussian processes, with methods for estimation and application to ocean wave data.

Findings

Model reproduces key statistical features of wave data

Accurately captures asymmetries between wave crests and troughs

Fits well to shallow water wave observations

Abstract

Many records in environmental sciences exhibit asymmetric trajectories and there is a need for simple and tractable models which can reproduce such features. In this paper we explore an approach based on applying both a time change and a marginal transformation on Gaussian processes. The main originality of the proposed model is that the time change depends on the observed trajectory. We first show that the proposed model is stationary and ergodic and provide an explicit characterization of the stationary distribution. This result is then used to build both parametric and non-parametric estimates of the time change function whereas the estimation of the marginal transformation is based on up-crossings. Simulation results are provided to assess the quality of the estimates. The model is applied to shallow water wave data and it is shown that the fitted model is able to reproduce important statistics of the data such as its spectrum and marginal distribution which are important quantities for practical applications. An important benefit of the proposed model is its ability to reproduce the observed asymmetries between the crest and the troughs and between the front and the back of the waves by accelerating the chronometer in the crests and in the front of the waves.