· 2016
Recurrent Marked Temporal Point Processes
- 557citations
- 2016year
Short summary
This paper introduces Recurrent Marked Temporal Point Processes (RMTPP) to model event data with covariates, overcoming limitations of fixed parametric assumptions in traditional models.
AI-generated from the title and abstract; the full text is not read.
Key points
- Introduces Recurrent Marked Temporal Point Processes (RMTPP) for modeling event data with covariates.
- Addresses limitations of traditional point process models, which often make restrictive parametric assumptions.
- Utilizes recurrent neural networks to learn complex temporal dynamics from data.
- Aims to provide a more expressive and flexible model for analyzing event sequences.
AI-generated from the title and abstract; the full text is not read.
Abstract
Large volumes of event data are becoming increasingly available in a wide variety of applications, such as healthcare analytics, smart cities and social network analysis. The precise time interval or the exact distance between two events carries a great deal of information about the dynamics of the underlying systems. These characteristics make such data fundamentally different from independently and identically distributed data and time-series data where time and space are treated as indexes rather than random variables. Marked temporal point processes are the mathematical framework for modeling event data with covariates. However, typical point process models often make strong assumptions about the generative processes of the event data, which may or may not reflect the reality, and the specifically fixed parametric assumptions also have restricted the expressive power of the respective processes. Can we obtain a more expressive model of marked temporal point processes? How can we learn such a model from massive data?
The authors' abstract, as published at the source., 2016 · DOI ↗
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Field: Applied Mathematics
Applied MathematicsMathematics