arXiv:2603. 14014v2 Announce Type: replace Abstract: We introduce Aumann-SHAP, an interaction-aware framework that decomposes counterfactual transitions by restricting the model to a local hypercube connecting baseline and counterfactual features.
By Adam Belahcen, St\'ephane Mussard
GroupSegment-SHAP (GS‑SHAP) introduces explanatory units called group‑segment players that capture cross‑variable dependence and distribution shifts over time in multivariate time‑series models. By attributing Shapley values to these units, GS‑SHAP preserves joint structural signals that traditional time‑series SHAP variants fragment. Experiments on human activity recognition, power‑system forecasting, medical signal analysis, and financial time series show that GS‑SHAP improves deletion‑based faithfulness by about 1.7× and reduces runtime by roughly 40% compared to existing baselines, while a financial case study demonstrates its ability to reveal interpretable multivariate‑temporal interactions during high‑volatility periods.
By Jinwoong Kim, Sangjin Park
arXiv:2608. 16098v1 Announce Type: cross Abstract: Multivariate time-series forecasting faces a structural dilemma: sharing one temporal predictor across variables is parameter-efficient but forces heterogeneous variables through an identical history-to-future map, whereas learning an independent predictor per variable restores flexibility at a cost that grows with the product of variable count, context length, and horizon.
By Xiachong Lin, Du Yin, Hao Xue, Wen Hu, Imran Razzak, Arian Prabowo, Matthew Amos, Flora D. Salim
The paper introduces Horizon-Resolved eXplanation (HRX), a framework that adds a horizon axis to time‑series forecasting explanations, allowing each forecast step to have its own importance map. HRX operates as a plug‑in for any differentiable forecaster, includes an evaluation protocol that tests the impact of removing top‑ranked inputs, and a rank criterion to decide when horizon resolution is beneficial. Experiments across multiple backbones and datasets demonstrate that incorporating the horizon axis improves explanation quality and that the step‑wise dependence is low‑dimensional, requiring only a few shared maps regardless of forecast length.
By Seunghan Lee, Jun Seo, Jaehoon Lee, Junhyeok Kang, Sangjun Han, Sungdong Yoo, Minjae Kim, Tae Yoon Lim, Dongwan Kang, Hwanil Choi, Soonyoung Lee, Wonbin Ahn
Predictive models are increasingly embedded in operational decision-making, yet standard explanation methods typically explain forecasts rather than the decisions those forecasts induce. This distinction is important in predict-then-optimize systems: large forecast changes may leave the optimizer's action unchanged, while small changes can alter the selected decision and its realized value.
arXiv:2606. 29878v1 Announce Type: new Abstract: Predictive models are increasingly embedded in operational decision-making, yet standard explanation methods typically explain forecasts rather than the decisions those forecasts induce.
By Konstantinos Ziliaskopoulos, Alexander Vinel, Alice E. Smith