论文信息 - A SIGNIFICANCE TEST FOR CLASSIFYING ARMA MODELS

A SIGNIFICANCE TEST FOR CLASSIFYING ARMA MODELS

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.

Elizabeth Ann Maharaj | E. Maharaj

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