# Likelihood-based inference and forecasting for trawl processes: a stochastic optimization approach

arXiv: https://arxiv.org/abs/2308.16092 · HTML: https://mlsanity.com/paper/2308.16092/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 44 references that are in the map.
- First posted on arXiv 30 Aug 2023.

## Builds on (most cited first, 12 of 12)

- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Monte Carlo Gradient Estimation in Machine Learning](https://mlsanity.com/paper/1906.10652/) (2019)
- [Implicit Reparameterization Gradients](https://mlsanity.com/paper/1805.08498/) (2018)
- [Pathwise Derivatives Beyond the Reparameterization Trick](https://mlsanity.com/paper/1806.01851/) (2018)
- [The Generalized Reparameterization Gradient](https://mlsanity.com/paper/1610.02287/) (2016)
- [Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms](https://mlsanity.com/paper/1610.05683/) (2016)
- [Stochastic gradient variational Bayes for gamma approximating distributions](https://mlsanity.com/paper/1509.01631/) (2015)
- [Fast Second-Order Stochastic Backpropagation for Variational Inference](https://mlsanity.com/paper/1509.02866/) (2015)
- [Spatio-temporal Ornstein-Uhlenbeck processes: theory, simulation and statistical inference](https://mlsanity.com/paper/1504.08327/) (2015)
- [Inference and forecasting for continuous-time integer-valued trawl processes](https://mlsanity.com/paper/2107.03674/) (2021)
- [Simulation methods and error analysis for trawl processes and ambit fields](https://mlsanity.com/paper/2208.08784/) (2022)
- [Nonparametric estimation of trawl processes: Theory and applications](https://mlsanity.com/paper/2209.05894/) (2022)

## Abstract (arXiv)

We consider trawl processes, which are stationary and infinitely divisible stochastic processes and can describe a wide range of statistical properties, such as heavy tails and long memory. In this paper, we develop the first likelihood-based methodology for the inference of real-valued trawl processes and introduce novel deterministic and probabilistic forecasting methods. Being non-Markovian, with a highly intractable likelihood function, trawl processes require the use of composite likelihood functions to parsimoniously capture their statistical properties. We formulate the composite likelihood estimation as a stochastic optimization problem for which it is feasible to implement iterative gradient descent methods. We derive novel gradient estimators with variances that are reduced by several orders of magnitude. We analyze both the theoretical properties and practical implementation details of these estimators and release a Python library which can be used to fit a large class of trawl processes. In a simulation study, we demonstrate that our estimators outperform the generalized method of moments estimators in terms of both parameter estimation error and out-of-sample forecasting error. Finally, we formalize a stochastic chain rule for our gradient estimators. We apply the new theory to trawl processes and provide a unified likelihood-based methodology for the inference of both real-valued and integer-valued trawl processes.