NEW AR/ARMA MODEL SELECTION METHOD FOR FINITE LENGTH TIME SERIES WITH APPLICATION TO PREDICTION OF PRETERM DELIVERIES

Abstract

In this paper, we proposed a new method able to select, simultaneously, from finite length time-series sequence, the suitable model type; pure Autoregressive (AR) or Autoregressive Moving Average (ARMA), which best describes the process that generated data. This is also a new way for estimating ARMA model order. The proposed method is different in its concept from all other methods found in the literature, direct and far simpler to implement. We proposed new empirical criteria basing on specific AR order selection criteria for finite length time series. We tested the proposed criteria on 10000 realizations of different broadband and narrow band processes. We also studied effects of additive noise and data length on their performance. The best mean recognition rate of model type was 92%. [Formula: see text]-value of Fisher’s exact test was always strictly under [Formula: see text]. Power spectral densities of selected models managed to recover the true Power spectral density (PSDs) shapes by resolving close spectral peaks and by finding their positions and their bandwidths with negligible spectral biases. Efficiency of our method was proved by its application in automatic segmentation of electrohysterographic signals (EHG) in the aim of predicting preterm deliveries with a mean rate of 90%.