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<p>In the dynamic landscape of international trade, accurate value prediction is crucial for decision-making and policy formulation. Deep learning (DL) models have shown significant promise in this domain due to their ability to capture complex patterns in large datasets. However, the reliability of these models can be compromised by data drift, a phenomenon in which the statistical properties of the input data change over time, leading to model degradation. This paper presents a data drift-aware framework designed to safeguard DL models used for international trade value prediction. By continuously monitoring and adapting to shifts in data distributions, our method ensures the robustness and accuracy of forecasts over time. Using real-world trade data from the United Nations Comtrade Database, we study the performance of a graph attention network (GAT) trained on historical trade flows from 2017 to 2019 and tested on drifted datasets from 2020 to 2023. This study extends our previous work by introducing a post-deployment monitoring layer based on three complementary drift metrics—the Kolmogorov–Smirnov (KS) statistic, the Population Stability Index (PSI), and the Jensen–Shannon (JS) divergence. Rather than relying on a single indicator, the framework employs a consensus-based adaptive retraining policy that triggers model updates only when multiple drift metrics exceed their thresholds simultaneously, whereas single-metric breaches trigger an early-warning state. This design enables the model to remain reliable under evolving trade conditions, including shocks related to COVID-19 and geopolitical disruptions. In addition to its methodological contributions, the framework explicitly addresses the computational demands of large-scale trade forecasting. Processing 55.4 million HS-6 observations and training GAT models with multi-head attention requires GPU-accelerated computation, parallel graph sampling, and high-memory resources. The continuous computation of KS, PSI, and JS divergence across thousands of country–partner–product combinations further necessitates high-performance computing (HPC) capabilities to support near-real-time drift detection and timely retraining. The results demonstrate that integrating multi-metric drift detection significantly enhances the predictive stability and resilience of the GAT model, providing a robust tool for stakeholders in international trade analysis. This study underscores the importance of adaptive monitoring mechanisms in DL applications and offers recommendations for developing drift-aware decision-support systems.</p>

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Observability of a prediction model post-deployment data drift: the case of international trade value

  • 期刊论文

  • 发表日期：

    2026年3月11日

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  • Bassem Sellami,
  • Chahinez Ounoughi,
  • Tarmo Kalvet,
  • Marek Tiits,
  • Sadok Ben Yahia

摘要

In the dynamic landscape of international trade, accurate value prediction is crucial for decision-making and policy formulation. Deep learning (DL) models have shown significant promise in this domain due to their ability to capture complex patterns in large datasets. However, the reliability of these models can be compromised by data drift, a phenomenon in which the statistical properties of the input data change over time, leading to model degradation. This paper presents a data drift-aware framework designed to safeguard DL models used for international trade value prediction. By continuously monitoring and adapting to shifts in data distributions, our method ensures the robustness and accuracy of forecasts over time. Using real-world trade data from the United Nations Comtrade Database, we study the performance of a graph attention network (GAT) trained on historical trade flows from 2017 to 2019 and tested on drifted datasets from 2020 to 2023. This study extends our previous work by introducing a post-deployment monitoring layer based on three complementary drift metrics—the Kolmogorov–Smirnov (KS) statistic, the Population Stability Index (PSI), and the Jensen–Shannon (JS) divergence. Rather than relying on a single indicator, the framework employs a consensus-based adaptive retraining policy that triggers model updates only when multiple drift metrics exceed their thresholds simultaneously, whereas single-metric breaches trigger an early-warning state. This design enables the model to remain reliable under evolving trade conditions, including shocks related to COVID-19 and geopolitical disruptions. In addition to its methodological contributions, the framework explicitly addresses the computational demands of large-scale trade forecasting. Processing 55.4 million HS-6 observations and training GAT models with multi-head attention requires GPU-accelerated computation, parallel graph sampling, and high-memory resources. The continuous computation of KS, PSI, and JS divergence across thousands of country–partner–product combinations further necessitates high-performance computing (HPC) capabilities to support near-real-time drift detection and timely retraining. The results demonstrate that integrating multi-metric drift detection significantly enhances the predictive stability and resilience of the GAT model, providing a robust tool for stakeholders in international trade analysis. This study underscores the importance of adaptive monitoring mechanisms in DL applications and offers recommendations for developing drift-aware decision-support systems.