Symbolic time series analysis via wavelet-based partitioning
نویسندگان
چکیده
منابع مشابه
Symbolic time series analysis via wavelet-based partitioning
Symbolic time series analysis (STSA) of complex systems for anomaly detection has been recently introduced in literature. An important feature of the STSA method is extraction of relevant information, imbedded in the measured time series data, to generate symbol sequences. This paper presents a wavelet-based partitioning approach for symbol generation, instead of the currently practiced method ...
متن کاملPattern identification in dynamical systems via symbolic time series analysis
This paper presents symbolic time series analysis (STSA) of multi-dimensional measurement data for pattern identification in dynamical systems. The proposed methodology is built upon concepts derived from Information Theory and Automata Theory. The objective is not merely to classify the time series patterns but also to identify the variations therein. To achieve this goal, a symbol alphabet is...
متن کاملSymbolic Analysis of High-Dimensional Time Series
In order to extract and to visualize qualitative information from a highdimensional time series, we apply ideas from symbolic dynamics. Counting certain ordinal patterns in the given series, we obtain a series of matrices whose entries are symbol frequencies. This matrix series is explored by simple methods from nominal statistics and information theory. The method is applied to detect and to v...
متن کاملWavelet-based Multifractal Analysis of RR Time Series
In this paper are presented the current results of scientific research of the RR time series for healthy and unhealthy subjects. TheRR intervals are obtained from 24-hour digital Holter ECG records of subjects. The used in the presented research work wavelet-based multifractal analysis of RR time series is provided by Wavelet Transform Modulus Maxima method. This method is based on wavelet anal...
متن کاملComparing time series using wavelet-based semblance analysis
Similarity measures are becoming increasingly commonly used in comparison of multiple datasets from various sources. Semblance filtering compares two datasets on the basis of their phase, as a function of frequency. Semblance analysis based on the Fourier transform suffers from problems associated with that transform, in particular its assumption that the frequency content of the data must not ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Signal Processing
سال: 2006
ISSN: 0165-1684
DOI: 10.1016/j.sigpro.2006.01.014