In the previous post I highlighted a wavelet based feature extraction model for financial time series. The intention was to use it as a starting point for developing an improved model. To start with let me explain what the model referred to in the previous post is meant to do.

There are two sections to the model. Common to both sections is a DWT smoother which is basically a lowpass filter. The first section analyses the smoothed volatility of the time series to determine the break points which they refer to as “variance change”. The second section performs FFT decomposition on the smoothed time series to obtain the dominant cycle. The trend and turning points are determined from the smoothed time series also.

Here is what I think about the model

  • Firstly I don’t support the researchers’ choice of applying the Fourier Transform to determine the dominant cycle period. I explain in this post why FFTs should be avoided when analysing financial time series data. I am in favour of the Hilbert transform mainly for two reasons:
    1. The Hilbert transform works under the assumption that each price point has a phase difference to the previous and subsequant price points. This allows evaluation of the cycle length on a bar-by-bar basis, which unlike the FFT does not impose a constraint on the observation window length.
    2. The Hilbert transform provides a way of visualising interaction of short term cycles with longer term cycles on a phasor plot.
  • What I wish to do is to replace the FFT part with the Hilbert Transform and another algorithm to determine the phasor plot of the actual time series - as shown below:

wavModel.png

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