  Time series anomaly detection is an important task, with applications in a
broad variety of domains. Many approaches have been proposed in recent years,
but often they require that the length of the anomalies be known in advance and
provided as an input parameter. This limits the practicality of the algorithms,
as such information is often unknown in advance, or anomalies with different
lengths might co-exist in the data. To address this limitation, previously, a
linear time anomaly detection algorithm based on grammar induction has been
proposed. While the algorithm can find variable-length patterns, it still
requires preselecting values for at least two parameters at the discretization
step. How to choose these parameter values properly is still an open problem.
In this paper, we introduce a grammar-induction-based anomaly detection method
utilizing ensemble learning. Instead of using a particular choice of parameter
values for anomaly detection, the method generates the final result based on a
set of results obtained using different parameter values. We demonstrate that
the proposed ensemble approach can outperform existing grammar-induction-based
approaches with different criteria for selection of parameter values. We also
show that the proposed approach can achieve performance similar to that of the
state-of-the-art distance-based anomaly detection algorithm.
