Simplified time series representations for efficient analysis of industrial process data
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Joint segmentation of wind speed and direction using a hierarchical model
Computational Statistics & Data Analysis
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A new criterion for off-line segmentation of signals is proposed. The derivation is general in the sense that it is valid for signals that are parameterized by linear or nonlinear functions embedded in additive noise, be it non-white or non-Gaussian. In addition, a penalty function is developed whose terms are easily justified and interpreted. As a special case, a criterion for segmentation of polynomial signals in white Gaussian noise is analyzed and compared with the AIC and MDL. The simulation results show that our criterion markedly outperforms its popular counterparts.