TimeInteract introduces a new regime called Time-Series Interaction, enabling models to continuously perceive incoming time-series data and user intent, decide when to respond, and keep processing new observations during response generation. The system employs a dual-view streaming encoder, a response control mechanism, and a decoupled inference pipeline to avoid blocking. Evaluated on the newly created StreamTSI-34K dataset, TimeInteract outperforms existing LLMs, VLMs, and TSLMs across four interaction levels, achieving significant gains in accuracy, response triggering, and inference speed.
By Sheng Pan, Yongli Gu, Yiqing Guo, Warren Jin, Bo Du, Shirui Pan, Ming Jin
The paper proposes a new approach to window‑level monitoring of temporal processes by treating it as a reference‑based hypothesis test. Instead of relying on predefined parametric models, the method uses an empirical reference distribution derived from task‑ or domain‑specific data, combined with pretrained time‑series encoders, kernel density estimation, and conformal calibration to provide finite‑sample valid inference in a learned representation space. Classical concepts such as stationarity and cyclostationarity naturally emerge as special cases of this framework, and experiments show the method’s sensitivity to distributional changes while maintaining well‑calibrated inference under stable conditions.
By Jinmyeong Choi, Taesup Kim, Artur Dubrawski
arXiv:2603. 11756v2 Announce Type: replace Abstract: Deep generative models for anomaly detection in multivariate time-series are typically trained by maximizing observed data likelihood.
By David Baumgartner, Eliezer de Souza da Silva, I\~nigo Urteaga
arXiv:2606. 16863v1 Announce Type: new Abstract: Evaluation of spatiotemporal point process (STPP) models relies heavily on opaque real-world datasets, where latent generative structure is unknown and model failures are difficult to attribute.
By Yahya Aalaila, Sumantrak Mukherjee, Gerrit Gro{\ss}mann, Sebastian Vollmer
arXiv:2606. 28670v1 Announce Type: cross Abstract: We introduce MACROCAST, a lightweight Time Series Foundation Model (TSFM) for real-time macroeconomic forecasting.
By Andrea Carriero, Davide Pettenuzzo, Shubhranshu Shekhar
arXiv:2602.01605v2 Announce Type: replace
Abstract: Time Series Foundation Models (TSFMs) leverage extensive pretraining to accurately predict unseen time series during inference, without the need fo...
By Anthony Bao, Venkata Hasith Vattikuti, Jeffrey Lai, William Gilpin