Autocorrelated Optimize-via-Estimate: Predict-then-Optimize versus Finite-sample Optimal

• URL: http://arxiv.org/abs/2602.01877v1
• Date: Mon, 02 Feb 2026 09:49:51 GMT
• Title: Autocorrelated Optimize-via-Estimate: Predict-then-Optimize versus Finite-sample Optimal
• Authors: Zichun Wang, Gar Goei Loke, Ruiting Zuo,
• Abstract summary: Models that directly optimize for out-of-sample performance in the finite-sample regime have emerged as a promising alternative to traditional estimate-then-optimize approaches.<n>We compare their performance in the context of autocorrelated uncertainties, specifically, under a Vector Autoregressive Moving Average VARMA(p,q) process.
• Score: 2.0228793142608588
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Models that directly optimize for out-of-sample performance in the finite-sample regime have emerged as a promising alternative to traditional estimate-then-optimize approaches in data-driven optimization. In this work, we compare their performance in the context of autocorrelated uncertainties, specifically, under a Vector Autoregressive Moving Average VARMA(p,q) process. We propose an autocorrelated Optimize-via-Estimate (A-OVE) model that obtains an out-of-sample optimal solution as a function of sufficient statistics, and propose a recursive form for computing its sufficient statistics. We evaluate these models on a portfolio optimization problem with trading costs. A-OVE achieves low regret relative to a perfect information oracle, outperforming predict-then-optimize machine learning benchmarks. Notably, machine learning models with higher accuracy can have poorer decision quality, echoing the growing literature in data-driven optimization. Performance is retained under small mis-specification.

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