Abstract
The paper compares recursive methods for detecting change points in environmental time series. Timely identification of peaks and troughs is important for planning defense actions and preventing risks. We consider linear nonparametric methods, such as time-varying coefficients, double exponential smoothers and prediction error statistics. These methods are often used in surveillance, forecasting and control, and their common features are sequential computation and exponential weighting of data. The new approach proposed here is to select their coefficients by maximizing the difference between subsequent peaks and troughs detected on past data. We compare the methods with applications to meteorological, astronomical and ecological data, and Monte-Carlo simulations.
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