Deep Optimal Timing Strategies for Time Series

TL;DR

This paper introduces a novel approach combining probabilistic time series forecasting with an optimal stopping framework using RNNs to determine the best future execution times, applicable to various business scenarios.

Contribution

It presents the first systematic method integrating probabilistic forecasting and optimal stopping with neural networks, avoiding complex dynamic models.

Findings

Effective in predicting optimal execution times

Reduces operational costs in practical applications

Flexible and data-driven approach

Abstract

Deciding the best future execution time is a critical task in many business activities while evolving time series forecasting, and optimal timing strategy provides such a solution, which is driven by observed data. This solution has plenty of valuable applications to reduce the operation costs. In this paper, we propose a mechanism that combines a probabilistic time series forecasting task and an optimal timing decision task as a first systematic attempt to tackle these practical problems with both solid theoretical foundation and real-world flexibility. Specifically, it generates the future paths of the underlying time series via probabilistic forecasting algorithms, which does not need a sophisticated mathematical dynamic model relying on strong prior knowledge as most other common practices. In order to find the optimal execution time, we formulate the decision task as an optimal stopping problem, and employ a recurrent neural network structure (RNN) to approximate the optimal times. Github repository: \url{github.com/ChenPopper/optimal_timing_TSF}.