1 research outputs found
Anomaly Detection in Trajectory Data with Normalizing Flows
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.Comment: Accepted as a conference paper at 2020 International Joint Conference
on Neural Networks (IJCNN 2020), part of 2020 IEEE World Congress on
Computational Intelligence (IEEE WCCI 2020