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A lot of the parameterization time is spent on building up the initial guesses for curve fitting. When performing sliding window parameterization, we can potentially speed things up quite a bit by taking advantage of the strong temporal correlations in our data by using the fitted parameters from time-step (t-1) as the guess parameters for the current time window (t).
The text was updated successfully, but these errors were encountered:
A lot of the parameterization time is spent on building up the initial guesses for curve fitting. When performing sliding window parameterization, we can potentially speed things up quite a bit by taking advantage of the strong temporal correlations in our data by using the fitted parameters from time-step (t-1) as the guess parameters for the current time window (t).
The text was updated successfully, but these errors were encountered: