Authors: Soumalya Sarkar Pritthi Chattopdhyay Asok Ray
Publish Date: 2016/08/19
Volume: 10, Issue: 8, Pages: 1535-1542
Abstract
The underlying theory of symbolic time series analysis STSA has led to the development of signal representation tools in the paradigm of dynamic datadriven application systems DDDAS where time series of sensor signals are partitioned to obtain symbol strings that in turn lead to the construction of probabilistic finite state automata PFSA Although various methods for construction of PFSA from symbol strings have been reported in literature similar efforts have not been expended on identification of an appropriate alphabet size for partitioning of time series so that the symbol strings can be optimally or suboptimally generated in a specified sense The paper addresses this critical issue and proposes an informationtheoretic procedure for partitioning of time series to extract lowdimensional features where the key idea is suboptimal identification of boundary locations of the partitioning segments via maximization of the mutual information between the state probability vector of PFSA and the members of the pattern classes Robustness of the symbolization process has also been addressed The proposed alphabet size selection and time series partitioning algorithm have been validated by two examples The first example addresses parameter identification in a simulated Duffing system with sinusoidal input excitation The second example is built upon an ensemble of time series of chemiluminescence data to predict lean blowout LBO phenomena in a laboratoryscale swirlstabilized combustor apparatus
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