A significance test for classifying ARMA models

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Given that the Euclidean distance between the parameter estimates of autoregressive expansions of autoregressive moving average models can be used to classify stationary time series into groups, a test of hypothesis is proposed to determine whether two stationary series in a particular group have significantly different generating processes. Based on this test a new clustering algorithm is also proposed. The results of Monte Carlo simulations are given.

Original languageEnglish
Pages (from-to)305-331
Number of pages27
JournalJournal of Statistical Computation and Simulation
Issue number4
Publication statusPublished - 1 Jan 1996


  • ARMA models
  • Significance test
  • Time series

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