The wavelet coefficients yield information as to the correlation between the wavelet (at a certain scale) and the data array (at a particular location). A larger positive amplitude implies a higher positive correlation, while a large negative amplitude implies a high negative correlation.
A useful way to determine the distribution of energy within the data array is to plot the wavelet power, equivalent to the amplitude-squared. By looking for regions within the Wavelet Power Spectrum (WPS) of large power, you can determine which features of your signal are important and which can be ignored.
Given the wavelet transform Wi of a multi-dimensional data array, Ai, where i=0...N-1 is the index and N is the number of points, then the Wavelet Power Spectrum is defined as the absolute-value squared of the wavelet coefficients, |Wi|2.
For a vector (such as a time series) the coefficients of wavelet power can be rearranged to yield a two-dimensional picture, where the first dimension is the independent variable (e.g. time) and the second dimension is the wavelet scale (e.g. 1/frequency).
The wavelet transform of a 2D array is also two-dimensional, and is arranged so that the smallest scales are in the upper-right quadrant (assuming that index [0, 0] is in the lower-left).
Use the "Chirp" dataset that is included in the Wavelet sample file. This dataset contains a time series with a sine wave that has an exponentially-increasing frequency. You can use the Multiresolution Analysis viewer to examine the time series.
Try the following steps: