Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Mining Heterogeneous Multivariate Time-Series for Learning Meaningful
  Patterns: Application to Home Health Telecare   For the last years, time-series mining has become a challenging issue for
researchers. An important application lies in most monitoring purposes, which
require analyzing large sets of time-series for learning usual patterns. Any
deviation from this learned profile is then considered as an unexpected
situation. Moreover, complex applications may involve the temporal study of
several heterogeneous parameters. In that paper, we propose a method for mining
heterogeneous multivariate time-series for learning meaningful patterns. The
proposed approach allows for mixed time-series -- containing both pattern and
non-pattern data -- such as for imprecise matches, outliers, stretching and
global translating of patterns instances in time. We present the early results
of our approach in the context of monitoring the health status of a person at
home. The purpose is to build a behavioral profile of a person by analyzing the
time variations of several quantitative or qualitative parameters recorded
through a provision of sensors installed in the home.

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The categories of this paper are: physics
37
The categories of this paper are: supr-con
32
The categories of this paper are: atom-ph
25
The categories of this paper are: patt-sol
36
The categories of this paper are: cmp-lg
18
The categories of this paper are: econ
31
The categories of this paper are: ao-sci
21
The categories of this paper are: adap-org
29
The categories of this paper are: funct-an
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The categories of this paper are: nlin
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The categories of this paper are: hep-th
24
The categories of this paper are: dg-ga
35
The categories of this paper are: mtrl-th
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The categories of this paper are: solv-int
33
The categories of this paper are: plasm-ph
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The categories of this paper are: acc-phys