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Denoting the syndromic time series on day number n by y[ n] the k-step ahead prediction problem is to estimate future values of the clinical data y ^ [ n + k], k > 0, using past days' values of the same clinical data.
The "prediction" problem, the subject of the present paper, refers to an attempt to estimate future values of the clinical data using today's and past days' values of the OTC channels, i.e. the predicted quantity is now, k > 0. In the parlance of linear filter theory, the data set whose prediction is desired (the dependent variable) is termed the primary data channel.
The "prediction" problem attempts to estimate future values of the clinical data using today's and past days' values of the OTC channels, i.e. the predicted quantity is, k > 0, which is then to be compared to the actual value of the clinical data on day number n + k.
Denoting the clinical data time series on day number n by y[ n] the N-step ahead prediction problem is to estimate future values of the clinical data y ^ [ n + N], N > 0, using a linear prediction equation (3) y ^ [ n + N ] = ∑ m = 0 M − 1 h [ m ] y [ n − m ], N > 0 and the recursive least squares method to compute the filter coefficients adaptively.
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