Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Scalable changepoint and anomaly detection in cross-correlated data with
  an application to condition monitoring   Motivated by a condition monitoring application arising from subsea
engineering we derive a novel, scalable approach to detecting anomalous mean
structure in a subset of correlated multivariate time series. Given the need to
analyse such series efficiently we explore a computationally efficient
approximation of the maximum likelihood solution to the resulting modelling
framework, and develop a new dynamic programming algorithm for solving the
resulting Binary Quadratic Programme when the precision matrix of the time
series at any given time-point is banded. Through a comprehensive simulation
study, we show that the resulting methods perform favourably compared to
competing methods both in the anomaly and change detection settings, even when
the sparsity structure of the precision matrix estimate is misspecified. We
also demonstrate its ability to correctly detect faulty time-periods of a pump
within the motivating application.

0
The categories of this paper are: hep-ph
8
The categories of this paper are: hep-ex
31
The categories of this paper are: ao-sci
12
The categories of this paper are: nucl-th
19
The categories of this paper are: eess
9
The categories of this paper are: nlin
2
The categories of this paper are: math
1
The categories of this paper are: cs
36
The categories of this paper are: cmp-lg
25
The categories of this paper are: patt-sol
32
The categories of this paper are: atom-ph
15
The categories of this paper are: nucl-ex
21
The categories of this paper are: adap-org
30
The categories of this paper are: q-alg
3
The categories of this paper are: physics
34
The categories of this paper are: chem-ph