arXiv:2607. 28124v1 Announce Type: new Abstract: As forecasts increasingly drive decisions in fields such as energy, transportation, and healthcare, understanding the historical data behind these predictions has become as crucial as the predictions themselves.
By Xu Zheng, Wei Cheng, Zhuomin Chen, Mo Sha, Jingchao Ni, Dongsheng Luo
arXiv:2511. 02152v2 Announce Type: replace Abstract: Time series data is one of the most popular data modalities in critical domains such as industry and medicine.
By Bart{\l}omiej Ma{\l}kus, Szymon Bobek, Grzegorz J. Nalepa
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: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
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