arXiv:2607. 01752v1 Announce Type: new Abstract: Temporal point processes (TPPs) have widespread applications across various domains.
By Cheng Wan, Quyu Kong, Feng Zhou
arXiv:2501. 14291v3 Announce Type: replace Abstract: Temporal point processes (TPPs) are stochastic process models used to characterize event sequences occurring in continuous time.
By Feng Zhou, Quyu Kong, Jie Qiao, Cheng Wan, Yixuan Zhang, Ruichu Cai
arXiv:2609.21382v1 Announce Type: new
Abstract: Operators of service-based systems act on forecasts of how a running execution will continue, and such a forecast is actionable only if its reliability...
By Jiaxin Yuan, Daniela Grigori, Han van der Aa
arXiv:2607. 28035v1 Announce Type: new Abstract: Irregular multivariate time series are widely encountered in applications such as healthcare monitoring, human activity recognition, and environmental sensing.
By Tianen Shen, Zhengyu Li, Yutong Li, Xiangfei Qiu, Xingjian Wu, Bin Yang, Jilin Hu
arXiv:2607. 05280v1 Announce Type: new Abstract: Many real-world systems evolve continuously, yet most machine learning models interpret time series as discrete sequences.
By Benjamin Walker
arXiv:2509. 24762v3 Announce Type: replace Abstract: Modeling event sequences of multiple event types with marked temporal point processes (MTPPs) provides a principled way to uncover governing dynamical rules and predict future events.
By David Berghaus, Patrick Seifner, Kostadin Cvejoski, C\'esar Ojeda, Rams\'es J. S\'anchez