Event and state detection in time series by genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Feng Xie",
  title =        "Event and state detection in time series by genetic
  school =       "Computer Science and Information Technology, RMIT
  year =         "2015",
  address =      "Australia",
  month =        jul,
  keywords =     "genetic algorithms, genetic programming, Time Series
                 Classification, Pattern Recognition, Event Detection,
                 Multi-Channel Time Series",
  URL =          "https://researchbank.rmit.edu.au/view/rmit:161490",
  URL =          "https://researchbank.rmit.edu.au/eserv/rmit:161490/Xie.pdf",
  size =         "214 pages",
  abstract =     "Event and state detection in time series has
                 significant value in scientific areas and real-world
                 applications. The aim of detecting time series event
                 and state patterns is to identify particular variations
                 of user-interest in one or more channels of time series
                 streams. For example, dangerous driving behaviours such
                 as sudden braking and harsh acceleration can be
                 detected from continuous recordings from inertial
                 sensors. However, the existing methods are highly
                 dependent on domain knowledge such as the size of the
                 time series pattern and a set of effective features.
                 Furthermore, they are not directly suitable for
                 multi-channel time series data. In this study, we
                 establish a genetic programming based method which can
                 perform classification on multi-channel time series
                 data. It does not need the domain knowledge required by
                 the existing methods.",
  abstract =     "The investigation consists of four parts: the
                 methodology, an evaluation on event detection tasks, an
                 evaluation on state detection tasks and an analysis on
                 the suitability for real-world applications. In the
                 methodology, a GP based method is proposed for
                 processing and analysing multi-channel time series
                 streams. The function set includes basic mathematical
                 operations. In addition, specific functions and
                 terminals are introduced to reserve historical
                 information, capture temporal dependency across time
                 points and handle dependency between channels. These
                 functions and terminals help the GP based method to
                 automatically find the pattern size and extract
                 features. This study also investigates two different
                 fitness functions - accuracy and area under the

                 The proposed method is investigated on a range of event
                 detection tasks. The investigation starts from
                 synthetic tasks such as detecting complete sine waves.
                 The performance of the GP based method is compared to
                 traditional classification methods. On the raw data the
                 GP based method achieves 100 percent accuracy, which
                 outperforms all the non-GP methods.The performance of
                 the non-GP methods is comparable to the GP based method
                 only with suitable features. In addition, the GP based
                 method is investigated on two complex real-world event
                 detection tasks - dangerous driving behaviour detection
                 and video shot detection. In the task of detecting
                 three dangerous driving behaviours from 21-channel time
                 series data, the GP based method performs consistently
                 better than the non-GP classifiers even when features
                 are provided. In the video shot detection task, the GP
                 based method achieves comparable performance on
                 11200-channel time series to the non-GP classifiers on
                 28 features. The GP based method is more accurate than
                 a commercial product.

                 The GP based method has also been investigated on state
                 detection tasks. This involves synthetic tasks such as
                 detecting concurrent high values in four of five
                 channels and a real-world activity recognition problem.
                 The results also show that the GP based method
                 consistently outperforms the non-GP methods even with
                 the presence of manually constructed features. As part
                 of the investigation, a mobile phone based activity
                 recognition data set was collected as there was no
                 existing publicly available data set.

                 The suitability of the GP based method for solving
                 real-world problems is further analysed. Our analysis
                 shows that the GP based method can be successfully
                 extended for multi-class classification. The analysis
                 of the evolved programs demonstrates that they do
                 capture time series patterns. On synthetic data sets,
                 the injected regularities are revealed in
                 understandable individuals. The best programs for three
                 real-world problems are more difficult to explain but
                 still provide some insight. The selection of relevant
                 channels and data points by the programs are consistent
                 with domain knowledge. In addition, the analysis shows
                 that the proposed method still performs well for time
                 series pattern of different sizes. The effective window
                 sizes of the evolved GP programs are close to the
                 pattern size. Finally, our study on execution
                 performance of the evolved programs shows that these
                 programs are fast in execution and are suitable for
                 real-time applications. In summary, the GP based method
                 is suitable for the kinds of real-world applications
                 studied in this thesis.

                 This thesis concludes that, with a suitable
                 representation, genetic programming can be an effective
                 method for event and state detection in multi-channel
                 time series for a range of synthetic and real-world
                 tasks. This method does not require much domain
                 knowledge such as the pattern size and suitable
                 features. It offers an effective classification method
                 in similar tasks that are studied in this thesis.",
  notes =        "Supervisors: Andy Song and Vic Ciesielski",

Genetic Programming entries for Feng Xie