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An application of neural point processes to geophysical data

Pierre-Alexandre Simon 1, 2 Radu Stoica 1, 3 Frédéric Sur 2
1 PASTA - Processus aléatoires spatio-temporels et leurs applications
Inria Nancy - Grand Est, IECL - Institut Élie Cartan de Lorraine : UMR 7502
2 TANGRAM - Recalage visuel avec des modèles physiquement réalistes
Inria Nancy - Grand Est, UL - Université de Lorraine, LORIA - ALGO - Department of Algorithms, Computation, Image and Geometry, CNRS - Centre National de la Recherche Scientifique
Abstract : The huge amount of temporal data available nowadays in numerous scientific fields requires dedicated analysis and prediction methods. Stochastic temporal point processes are certainly one of the popular approaches available to model time series. While point processes have been successfully applied in many application domains, they need strong assumptions. For instance, the conditional intensity is often supposed to follow a particular parametric function, hence fixing a priori the structure of the events distribution: purely random or independent, clustered or regular. Recent papers investigate the use of models from machine learning dedicated to sequential events analysis, namely recurrent neural networks (RNN). These RNNs are expected to be versatile enough to automatically adapt to the data, without the need for a priori choosing the character of the events distribution. This paper presents a brief introduction to the so-called neural point processes and discusses numerical experiments. In particular, the presented real data application considers seismic data from the Guadeloupe region.
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Submitted on : Wednesday, July 21, 2021 - 6:11:55 PM
Last modification on : Saturday, October 16, 2021 - 11:26:10 AM


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  • HAL Id : hal-03294911, version 1



Pierre-Alexandre Simon, Radu Stoica, Frédéric Sur. An application of neural point processes to geophysical data. 2021 RING Meeting, Sep 2021, Nancy, France. ⟨hal-03294911⟩



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