Learn how to detect dominant cycles with spectrum analysis
Learn how to detect dominant cycles with spectrum analysis
We will compare different spectrum analysis methods in regards to their performance of detecting exact cycle lengths (“frequency”) components.
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What you'll learn
- This course explains the key elements of a Fourier-based spectrum analysis.
- Understanding the basic computations involved in FFT-based or Goertzel-algorithm-based measurement.
- Explaining the core background of FFT in layman terms and concentrate on the important aspects on “how to read a spectrum” plot.
- Learn why the Goertzel algorithm outperforms classical Fourier transforms for the purpose of cycles detection in financial markets
Description
At the heart of almost every cycle analysis platform is a spectrum module.
Various derivatives of the Fourier transform are available. But which application of Fourier is the "best" for use in economic markets? This course tries to provide an answer.
Therefore, the course focuses on explaining the essential aspects in layman's terms:
- Fundamental aspects on "How to read a spectrum diagram" are at the center of the course.
- Different Fourier spectrum analysis methods are compared in terms of their performance in detecting exact cycle lengths ("frequency" components).
- Learn what is important in detecting cycles in the financial markets.
Understanding the basic calculations involved in measuring cycle length, knowing the correct scaling, correct non-integer interpolation, converting different units (frequency vs. time), and learning how to read spectral plots are all critical to the success of cycle analysis and related projection.
Being equipped with this knowledge will allow you to have more success with your custom cycle analysis application.
