A second-order stationary time series decomposes into a deterministic component and a one-sided square-summable linear filter of a stationary time series of its mutually orthogonal linear innovations. Normalize . For a causal invertible ARMA process with no deterministic component, the driving white noise is this innovation process: observations and innovations span the same closed past subspace, while the next innovation is orthogonal to it.
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