Anomaly Detection in Trajectory Data with Normalizing Flows
- URL: http://arxiv.org/abs/2004.05958v1
- Date: Mon, 13 Apr 2020 14:16:40 GMT
- Title: Anomaly Detection in Trajectory Data with Normalizing Flows
- Authors: Madson L. D. Dias, C\'esar Lincoln C. Mattos, Ticiana L. C. da Silva,
Jos\'e Ant\^onio F. de Macedo, Wellington C. P. Silva
- Abstract summary: We propose an approach based on normalizing flows that enables complex density estimation from data with neural networks.
Our proposal computes exact model likelihood values, an important feature of normalizing flows, for each segment of the trajectory.
We evaluate our methodology, named aggregated anomaly detection with normalizing flows (GRADINGS), using real world trajectory data and compare it with more traditional anomaly detection techniques.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The task of detecting anomalous data patterns is as important in practical
applications as challenging. In the context of spatial data, recognition of
unexpected trajectories brings additional difficulties, such as high
dimensionality and varying pattern lengths. We aim to tackle such a problem
from a probability density estimation point of view, since it provides an
unsupervised procedure to identify out of distribution samples. More
specifically, we pursue an approach based on normalizing flows, a recent
framework that enables complex density estimation from data with neural
networks. Our proposal computes exact model likelihood values, an important
feature of normalizing flows, for each segment of the trajectory. Then, we
aggregate the segments' likelihoods into a single coherent trajectory anomaly
score. Such a strategy enables handling possibly large sequences with different
lengths. We evaluate our methodology, named aggregated anomaly detection with
normalizing flows (GRADINGS), using real world trajectory data and compare it
with more traditional anomaly detection techniques. The promising results
obtained in the performed computational experiments indicate the feasibility of
the GRADINGS, specially the variant that considers autoregressive normalizing
flows.
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