Investigation on Performance of Change Point Algorithm in Time Series Dynamical Regimes and Effect of Data Characteristics

In this paper, Bayesian online inference in models of
data series are constructed by change-points algorithm, which
separated the observed time series into independent series and study
the change and variation of the regime of the data with related
statistical characteristics. variation of statistical characteristics of time
series data often represent separated phenomena in the some
dynamical system, like a change in state of brain dynamical reflected
in EEG signal data measurement or a change in important regime of
data in many dynamical system. In this paper, prediction algorithm
for studying change point location in some time series data is
simulated. It is verified that pattern of proposed distribution of data
has important factor on simpler and smother fluctuation of hazard
rate parameter and also for better identification of change point
locations. Finally, the conditions of how the time series distribution
effect on factors in this approach are explained and validated with
different time series databases for some dynamical system.

• Farhad Asadi
• Mohammad Javad Mollakazemi

• Time series
• fluctuation in statistical characteristics
• optimal learning.

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Accepted and oral presentation in ICARM 2014: XII International
Conference on Automation, Robotics and Mechatronics, October, 27-28,
2014, Istanbul, turkey.