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A real autoregressive signal is the output of a real autoregressive filter whose input is a real white noise. The same definition can be used for complex signals and systems, but the second order statistics of a complex white noise are not completely defined by its correlation function as in the real case. We present the consequences of this fact for autoregressive complex signals. In particular we show that the linear predictor of such signals is not necessarily a finite moving average filter.