# Optimal Stopping with Gaussian Processes

arXiv: https://arxiv.org/abs/2209.14738 · HTML: https://mlsanity.com/paper/2209.14738/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 43 references that are in the map.
- First posted on arXiv 22 Sep 2022.

## Builds on (most cited first, 4 of 4)

- [Get Real: Realism Metrics for Robust Limit Order Book Market Simulations](https://mlsanity.com/paper/1912.04941/) (2019)
- [Solving high-dimensional optimal stopping problems using deep learning](https://mlsanity.com/paper/1908.01602/) (2019)
- [Zap Q-Learning for Optimal Stopping Time Problems](https://mlsanity.com/paper/1904.11538/) (2019)
- [Optimal Purchasing Policy For Mean-Reverting Items in a Finite Horizon](https://mlsanity.com/paper/1711.03188/) (2017)

## Built on by (most cited first, 2 of 2)

- [Deep Optimal Timing Strategies for Time Series](https://mlsanity.com/paper/2310.05479/) (2023)
- [Deep Learning for the Multiple Optimal Stopping Problem](https://mlsanity.com/paper/2512.22961/) (2025)

## Abstract (arXiv)

We propose a novel group of Gaussian Process based algorithms for fast approximate optimal stopping of time series with specific applications to financial markets. We show that structural properties commonly exhibited by financial time series (e.g., the tendency to mean-revert) allow the use of Gaussian and Deep Gaussian Process models that further enable us to analytically evaluate optimal stopping value functions and policies. We additionally quantify uncertainty in the value function by propagating the price model through the optimal stopping analysis. We compare and contrast our proposed methods against a sampling-based method, as well as a deep learning based benchmark that is currently considered the state-of-the-art in the literature. We show that our family of algorithms outperforms benchmarks on three historical time series datasets that include intra-day and end-of-day equity stock prices as well as the daily US treasury yield curve rates.