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.
By Muyan Anna Li, Manikandan Ravikiran, Aditi Gautam
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.
By Hongnan Ma, Yiwei Shi, Mengyue Yang, Weiru Liu
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.
By Xu Zheng, Zichuan Liu, Zhuomin Chen, Mayur Akewar, Janki Bhimani, Jason Liu, Mo Sha, Jingchao Ni, Wei Cheng, Dongsheng Luo
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...
By Louis Peter, Nils Gumpfer, Jana Fischer, Christin Seifert, Jennifer Hannig
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. 07291v1 Announce Type: new Abstract: Multivariate time-series forecasting requires models to reason over temporal dynamics, cross-variable dependencies, and historical input-output correspondences.
By Tao Chen, Yexu Zhou, Zhi Gong, Hengwei He, Hongda Li, Zhewei Chen, Dongjing Wang, Xin Zhang, Decheng Liu, Chunlei Peng, Zheng Chen, Wenyue Ding