Learning the Number of Autoregressive Mixtures in Time Series Using the Gap Statistics

11 Sep 2015Jie DingMohammad NoshadVahid Tarokh

Using a proper model to characterize a time series is crucial in making accurate predictions. In this work we use time-varying autoregressive process (TVAR) to describe non-stationary time series and model it as a mixture of multiple stable autoregressive (AR) processes... (read more)

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