Estimate Menu

The Estimate Menu is used to estimate (interpolate) a data set using various procedures and also for AR autoregressive prediction.

The Spline Estimationoption offers eight important spline procedures for interpolation and smoothing. First and second derivatives are also available. This procedure is useful for creating uniformly spaced data sets since uniform data are not required.

The Fourier Estimation option offers interpolation and smoothed estimations based upon the frequency spectrum.

The Smoothed Data Spline Estimation procedure combines smoothing and B-spline estimation. Note that this is not a smoothing spline, but is rather the fitting of an interpolating B-spline to data that have been pre-smoothed.

The Local Regression Spline Estimation option offers an adjustable order Loess-type (locally-weighted least-squares) procedure. This procedure is also useful for creating uniformly spaced data sets since uniform data are not required.

The Savitzky-Golay Spline Estimation procedure combines the full capability of the Savitzky-Golay smoothing with a constrained spline interpolant. This procedure has been especially tailored for estimating derivatives.

The AR Modeling and Prediction procedure offers effective autoregressive forecasting and extrapolation. The AR algorithms include SVD (singular value decomposition) procedures for in-place noise removal. Multiple orders can be simultaneously plotted and stabilizations are available for roots that lie outside the unit circle. The points that are to be processed can be specified, allowing predictions based on a data segment to be compared with subsequent data. The extent of the prediction is variable and white noise can be added to test the robustness of an algorithm's prediction.