Inspection of time series at different scales. Requires one column of ordinal or continuous data with
even spacing of points.
The continuous wavelet transform (CWT) is an analysis method where a data set can be inspected at
small, intermediate and large scales simultaneously. It can be useful for detecting periodicities at
different wavelengths, self-similarity and other features. The vertical axis in the plot is a logarithmic
size scale (base 2), with the signal observed at a scale of only two consecutive data points at the top,
and at a scale of one fourth of the whole sequence at the bottom. One unit on this axis corresponds
to a doubling of the size scale. The top of the figure thus represents a detailed, fine-grained view,
while the bottom represents a smoothed overview of longer trends. Signal power (or more correctly
squared correlation strength with the scaled mother wavelet) is shown with a grayscale or in colour.
The shape of the mother wavelet can be set to Morlet (wavenumber 6), Paul (4
order) or DOG
(Derivative Of Gaussian, 2
derivative). The Morlet wavelet usually performs best.
The example above is based on a foram oxygen isotope record from 1 Ma to Recent, with an even
spacing of 0.003 Ma (3 ka). A band can be seen at a scale of about 2
=32 samples, or about 100 ka. A
weaker band around 2
=13 samples corresponds to a scale of about 40 ka. These are orbital
periodicities. In contrast with the ͞bulk͟ spectral analysis, the scalogram makes visible changes in
strength and frequency over time.
The so-called ͞cone of influence͟ can be plotted to show the region where boundary effects are
The 'Sample interval' value can be set to a value other than 1. This will only influence the scaling of
the labels on the x and y axes.