Time Menu

The Time Menu offers signal processing and analysis procedures that are primarily carried out in the time domain:

The Detrend option is used to fit a trend to the overall data and remove it. There are 8 parametric models which can be fitted with linear least squares or by one of two non-linear robust options that should be less influenced by the signal components. In addition to subtracting the fit, this option can zero the mean and/or normalize to unit standard deviation.

The Difference, Cumulative, Normalize option can difference the data with adjustable order and lag, compute various cumulatives, and normalize for unit area, unit power, unit standard deviation, and zero mean.

The Savitzky-Golay Smoothing Filter procedure offers effective time-domain smoothing for data sets with uniform X-spacing. The algorithm offers adjustable order (quartic is typical), automatic sequential passes (three is about optimum), and optional first through fourth smoothed derivatives.

The Spline Estimation option offers seven important spline procedures for interpolation and smoothing. First and second derivatives are also available. This procedure is useful for both upsampling and downsampling since the range and number of output points are specified. Uniform data are not required.

The Non-Parametric Estimation option offers an adjustable order Loess-type (locally-weighted least-squares) procedure. This procedure can sometimes extract an underlying low frequency data pattern in extremely noisy data.

The Autocorrelation option offers the means to inspect the estimated autocorrelation series. This is often helpful in determining whether or not a signal is distinguishable from white noise.

The AR Linear Prediction procedure offers effective forecasting and extrapolation. The AR algorithms include SVD (singular value decomposition) procedures for in-situ noise removal. Stabilizations are also 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 noise can be added to see how well the algorithm's prediction stands up when white noise is added.

The Fractal Dimension option computes the Hurst exponent, a measure of the fractal dimension of a data series. This can be used to assess whether or not a long-term memory effect exists in data that appear to evidence a flat white noise spectrum.