GRAIL is a region‑based framework for explaining time‑series models that moves beyond per‑timestep saliency by treating domain‑specific intervals—such as the P‑wave and QRS complex in ECG data—as players in a cooperative game. It describes each region with human‑readable features, generates counterfactual baselines using an on‑manifold conditional generator, and incorporates static covariates as singleton players to directly weigh tabular evidence against the signal. The method attributes predictions via Owen values with variance reduction, and experiments on synthetic and real data demonstrate faithful, robust explanations that provide meaningful knowledge to domain experts.
arXiv:2608. 25897v1 Announce Type: new Abstract: Explaining deep learning models operating on time series data is crucial in various applications that require transparent and interpretable insights into model behavior.
arXiv:2603. 06638v3 Announce Type: replace-cross Abstract: The rise of large language models (LLMs) has shifted time series analysis from narrow analytics to general-purpose reasoning.
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.
arXiv:2607. 21573v1 Announce Type: cross Abstract: Faithful explanations of time-series classifiers should identify subsequences that are not only sufficient to preserve a black-box model's prediction, but also necessary for maintaining it.
Explaining deep learning models operating on time series data is crucial in various applications that require transparent and interpretable insights into model behavior. {Existing explanation methods...
arXiv:2606. 18049v1 Announce Type: new Abstract: Decision-making with deep learning-based time series forecasting requires not only accurate predictions but also actionable insights.
NVExplain is a model‑agnostic framework that explains time‑series forecasting by attributing each forecast horizon to temporally relevant historical lags. It models forecasting as a latent trajectory, introduces semantic flow to track information evolution, and aggregates this into a lag‑horizon attribution matrix. The method also generates structure‑preserving perturbations and fits sparse local surrogates to produce human‑readable, temporally coherent explanations, and demonstrates competitive faithfulness and stability across benchmark datasets.
arXiv:2605.09395v3 Announce Type: replace Abstract: In this paper, we propose the first VL\underline{\textbf{M}} \underline{\textbf{a}}gentic \underline{\textbf{r}}easoning framework for few-\underli...
The paper introduces a dual‑view approach for time‑series language models, combining line plots that preserve absolute measurements with normalized delay embeddings that highlight temporal structure. By fusing these complementary visual representations through dedicated encoders and multi‑level feature fusion, the model achieves significant performance gains on several benchmarks, outperforming single‑view baselines and existing TSLMs. The results demonstrate that preserving both measurement frame and temporal structure is an effective design principle for general‑purpose time‑series reasoning.
Decision-making with deep learning-based time series forecasting requires not only accurate predictions but also actionable insights. However, current architectures do not inherently provide such information.
arXiv:2608.21449v1 Announce Type: new Abstract: Time series arise in a wide range of application domains and are analyzed using machine learning in decision-critical settings. Time series classificat...
arXiv:2607. 09880v1 Announce Type: cross Abstract: Clinical time series are central to patient monitoring, risk assessment, and clinical decision support.