978-1-5090-6126-6/16/$31.00 2016 IEEE 426
A SURVEY OF RESEARCH ON ANOMALY DETECTION FOR TIME SERIES
HU-SHENG WU
Materiel Engineering College, Armed Police Force Engineering University, Xi'an 710086, China
E-MAIL: wuhusheng0421@163.com
Abstract:
Time series is an important class of temporal data objects
and it can be easily obtained from scientific and financial
applications, and anomaly detection for time series is
becoming a hot research topic recently. This survey tries to
provide a structured and comprehensive overview of the
research on anomaly detection. In this paper, we have
discussed the definition of anomaly and grouped existing
techniques into different categories based on the underlying
approach adopted by each technique. And for each category,
we identify the advantages and disadvantages of the
techniques in that category. Then, we provide a briefly
discussion on the representative methods recently.
Furthermore, we also point out some key issues about
multivariate time series anomaly. Finally, some suggestions
about anomaly detection are discussed and future research
trends are also summarized, which is hopefully beneficial to
the researchers of time series and other relative domains.
Keywords:
Time series; anomaly detection; big data; data mining;
multivariate time series
1. Introduction
Date have proliferated over with the development of
information technology. According to the latest research of
University of Southern California in 2016, since 1980s, the
scale of data has increased sharply and doubled every year
or even a few months. All of the global data information
has reached 295EB until 2007, 1.8ZB until 2011, and will
reach more than 40ZB in 2020. Therefore, big data era has
arrived. And how to abstract potential information from the
plentiful data needs to be solved urgently [1]. A big part of
these data is time series. The so-called time series is a
sequence records according with the chronological order.
Time series is so widely applied in many fields, such as
military, economy and scientific observations that it has
aroused great attention concern from researchers.
For normal time series data, although the number of
abnormal data is very small, it does not mean that the
abnormal data is not important. On the contrary, some
important data may be hided behind these few abnormal
data. In medical field, the abnormal heart beat should be
detected so that doctors can find the disease in time by the
ECG observation. In addition, the anomaly detection of
time series can be widely used in the engine condition
monitoring, network intrusion detection, anti-money
laundering, network public opinion monitoring, credit card
fraud, stock market analysis, improper tax behavior
monitoring, major construction projects inspection, natural
disaster analysis and so on. Obviously, the study of
abnormal detection of time series has important theoretical
value and practical significance.
2. Definition of time series anomaly detection
Time series widely exist in various large financial,
medical, engineering and social science database. There are
two features between time series and other data types.
Firstly, the time attribute, the records of each variable must
have time dimension and should be arranged in
chronological order, and some date types like market basket
data do not have such attributes. Secondly, the sequence
attribute, the record values are a continuous one in a certain
period of time and in certain laws. Time series can be
divided into univariate time series and multivariate time
series according to the number of variables, the specific
definition is as follows:
Definition 1 Time Series A sequence records
according with the chronological order
(1), (2), , ( ), , ( )
i i i i
S v v v t v n …… ……
is defined as the time series.
In which t (t=1,2,…,n)
is defined as time,
i
(
1,2, ,im
)
is defined as variable.
()
i
vt
is defined as the record of the
variable
i
on time
t
. When m=1,
S
is defined as
univariate time series (UTS). When m>1,
S
is defined as
multivariate time series (MTS).
At the same time, in order to make the anomaly
detection of MTS meaningful, MTS needs to meet the three
requirements as follows: (1) For the MTS data set, the
variable dimension of each sequence is identical, and the
variables which have the same meaning are one-to-one
match. (2) For one MTS sample, the recording time of each
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