Linear Prediction
predict(v, m, n)—Returns a vector of n predicted values past the last value in v, assuming that the data are measured at equal time intervals.
The predict function uses Burg's method to calculate autocorrelation coefficients for the last m points in v, which are then used to predict the value of the (m + 1) point. This procedure repeats in a sliding window. This algorithm is useful when data is smooth and oscillatory, though not necessarily periodic.
Linear prediction can be used for extrapolation, but should not be confused with linear or polynomial extrapolation. The function predict can be used to estimate prior values by reversing the order of v.
v is a real data vector of equally spaced data samples.
m, n are positive integers, 0 < m < length(v) − 1. In practice, m should be much smaller than length(v). As you increase the number of predicted points n larger than m, predicted values are computed based only on previously predicted values, perhaps producing undesirable results.
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