Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Detecting Changes in Time Series Data using Volatility Filters   This work develops techniques for the sequential detection and location
estimation of transient changes in the volatility (standard deviation) of time
series data. In particular, we introduce a class of change detection algorithms
based on the windowed volatility filter. The first method detects changes by
employing a convex combination of two such filters with differing window sizes,
such that the adaptively updated convex weight parameter is then used as an
indicator for the detection of instantaneous power changes. Moreover, the
proposed adaptive filtering based method is readily extended to the
multivariate case by using recent advances in distributed adaptive filters,
thereby using cooperation between the data channels for more effective
detection of change points. Furthermore, this work also develops a novel change
point location estimator based on the differenced output of the volatility
filter. Finally, the performance of the proposed methods were evaluated on both
synthetic and real world data.

30
The categories of this paper are: q-alg
17
The categories of this paper are: q-fin
28
The categories of this paper are: alg-geom
36
The categories of this paper are: cmp-lg
10
The categories of this paper are: q-bio
13
The categories of this paper are: quant-ph
11
The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con
29
The categories of this paper are: funct-an
14
The categories of this paper are: math-ph
2
The categories of this paper are: math
31
The categories of this paper are: ao-sci
12
The categories of this paper are: nucl-th
9
The categories of this paper are: nlin
23
The categories of this paper are: solv-int
21
The categories of this paper are: adap-org