GroupSegment SHAP Boosts Interpretability of Multivariate Time-Series Models

Global: New GroupSegment SHAP Method Boosts Multivariate Time-Series Explainability

A new interpretability technique called GroupSegment SHAP (GS‑SHAP) has been introduced to improve the analysis of multivariate time‑series models across sectors such as healthcare, energy, and finance. The method was described in a preprint posted to arXiv on January 2026 (arXiv:2601.06114). It builds explanatory units that combine cross‑variable dependence with temporal segments, addressing limitations of existing SHAP variants that treat features and time independently.

Method Overview

The authors explain that GS‑SHAP defines “group‑segment players” based on statistical dependence among variables and detected distribution shifts over time. By aggregating these players, the technique quantifies contributions using the Shapley value framework, aiming to capture joint structural signals that span multiple variables and specific intervals.

Experimental Evaluation

Evaluation was conducted on four real‑world datasets: a human activity recognition benchmark, a power‑system load forecasting set, a medical signal collection, and a financial market series. In each case, the authors compared GS‑SHAP against KernelSHAP, TimeSHAP, SequenceSHAP, WindowSHAP, and TSHAP.

Performance Gains

Results indicate that GS‑SHAP improves deletion‑based faithfulness, measured by DeltaAUC, by approximately 1.7 times on average relative to the time‑series SHAP baselines. The improvement suggests that the explanations more accurately reflect the impact of feature‑time interactions on model predictions.

In addition to accuracy gains, the method reduces computational overhead. Under matched perturbation budgets, wall‑clock runtime decreased by roughly 40 percent on average compared with the baseline SHAP approaches, according to the authors’ experiments.

Financial Case Study

A case study focused on financial time series demonstrated that GS‑SHAP can surface interpretable multivariate‑temporal interactions among key market variables during periods of heightened volatility. The authors argue that such insights could aid analysts in understanding driver dynamics during turbulent market conditions.

Implications and Future Work

The preprint notes that the approach is model‑agnostic and can be integrated with any predictive model that processes multivariate time‑series data, though implementation details and code availability were not specified in the abstract. The authors suggest future work will explore automated detection of optimal group‑segment configurations and broader validation across additional domains.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.