Learn how to detect dominant cycles with spectrum analysis
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
- Basic cycle and/or spectrum analysis knowledge is helpfull, but not mandatory.
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.
There are many issues to consider when analyzing and measuring cycles in financial markets. Unfortunately, it is easy to make incorrect spectral measurements resulting in inaccurate cycle projections either on wrong phase or length gathered from the spectrum plot.
This course explains the key elements of a Fourier-based spectrum analysis.
You will learn why the Goertzel algorithm outperforms classical Fourier transforms for the purpose of cycles detection in financial markets.
Compared to an FFT, the Goertzel algorithm is simple and much more efficient for detecting cycles in data series related to financial markets. You will learn and understand why in this course.
Who this course is for:
- Data-science and financial market analysts interested in applying digital signal processing to analyzing and measuring cycles in financial markets
- Experts who want to understand the differences between standard Fourier and Goertzel algorithm (FFT vs. G-DFT)
EXPERT ON DIGITAL SIGNAL PROCESSING AND CYCLES DETECTION AND FORECASTING APPLICATIONS
Lars von Thienen is Member of the Board of the Foundation for the Study of Cycles. He holds a degree in engineering and management. He worked as a programmer, scientist, trader and business consultant for over 30 years. He has extensive experience in combining technical engineering know-how with economics and business-related issues.
Coding software since childhood, von Thienen has supported leading German DAX companies in developing their digital and business strategies as a large-scale project and IT manager.
He develops algorithms and software for cycle recognition at whentotrade and has published two books on cycles analysis.
Appointed by the Minister of Justice, von Thienen has been working as a commercial judge for over a decade. Von Thienen is based in Germany.
Founder and CEO of a German-based knowledge management company, Lars von Thienen invented the standalone, desktop search and knowledge management app.
Lars is developer and inventor of the first cloud-based cycles detection engine used by the Foundation for the Study of Cycles. He acts as host for the weekly YouTube channel: Market Cycles Report on Cycles TV. FSC Board Member Lars von Thienen analyzes current market data using the cycles app to foster a better understanding of how to apply cycles analysis.
He writes and comments regularly about current cycles junctures in the global stock market. In his blog "Beyond Market Cycles" on substack, von Thienen shares interesting cycles analysis charts with comments.