The Parameters Optimization of Filtered Derivative for Change Points Analysis

  •  Mohamed Elmi    


Let $\mathbf{X} = ( X_1,X_2,\ldots,X_N )$ be a time series. That is a sequence random variable  indexed by the time $t=(1,2,\ldots,N)$, we suppose that the parameters of $\mathbf{X}$ are piecewise constant. In other words, it exists a subdivision $\tau=(\tau_1< \tau_2<\ldots < \tau_K )$ such that $ X_i$ is a family  of independent and identically distributed (i.i.d) random variables for $i \in (\tau_k,\tau_{k+1}] $, and $k = 0,1,\ldots,K$ where by convention $\tau_o=0$ and $\tau_{K+1}=N $. The preceding works such that (Bertrand, 2000) control the probability of false alarms for minimizing the  probability  of type I error  of change point analysis. The novelty in this work is to control the number of false alarms.  We give an bound of number of false alarms and the necessary condition for number of no detection. In other hand, we know the filtered derivative (Basseville \& Nikirov, 1993) depends the parameters such that the threshold and the window, we give in order to choose the optimal parameters. We compare the results of Filtered Derivative optimized parameters and the Penalized Square Error methods in particulary the adaptive method of (Lavielle \& Teyssi\`ere,  2006).

This work is licensed under a Creative Commons Attribution 4.0 License.
  • ISSN(Print): 1927-7032
  • ISSN(Online): 1927-7040
  • Started: 2012
  • Frequency: bimonthly

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