Generate histograms for the Haar coefficients created by
applying the Haar transform to the time series for the
Applied Materials (symbol: AMAT) daily close price.
Moving from the high frequency coefficient spectrum
to the lower frequency spectrum, set each spectrum
to zero and output the regenerated time series to
a file.
The amount of Gaussian noise in the Haar wavelet coefficients can
be seen by graphing a histogram of the coefficients along with a
Gaussian (normal) curve.
The point class represents a coefficient value so that it can be
sorted for histogramming and then resorted back into the orignal
ordering (e.g., sorted by value and then sorted by index)
Output a gnuplot formatted histogram of the area under a normal
curve through the range m.low to m.high based on the
mean and standard deviation of the values in the array v.
Output a Haar spectrum where the x-axis is the sample value
number, the y-axis is the log2 of the window width and
the z-axis is the value (e.g., average or average difference).
Set the number of data elements in the array to
a new value (note that this will usually be
smaller than the array size, unless a power of
two is chosen for "new_size").
Test the experimental code to generate a normal curve
with the mean and standard deviation derived from
a coefficient spectrum (in this case the highest
frequency spectrum).
Generate histograms for the Haar coefficients created by
applying the Haar transform to the time series for the
Applied Materials (symbol: AMAT) daily close price.