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- W124403607 abstract "Adaptive algebraic level-set segmentation algorithm of financial time series is presented in this paper. The proposed algorithm is based on the algebraic one step-forward predictor with internal smoothing, which is used to identify a near optimal algebraic model. Particle swarm optimization algorithm is exploited for the detection of a base algebraic fragment of the time series. A combinatorial algorithm is used to detect intervals where predictions are lower than a predefined level. Moreover, the combinatorial algorithm does assess the simplicity of the identified near optimal algebraic model. Automatic adaptive identification of quasi-stationary segments can be employed for complex financial time series." @default.
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- W124403607 date "2014-01-01" @default.
- W124403607 modified "2023-09-27" @default.
- W124403607 title "Algebraic Level-Set Approach for the Segmentation of Financial Time Series" @default.
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- W124403607 doi "https://doi.org/10.1007/978-3-662-45523-4_20" @default.
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