Learn how to detect dominant cycles with spectrum analysis

Learn how to detect dominant cycles with spectrum analysis, available at $34.99, has an average rating of 3.95, with 11 lectures, based on 18 reviews, and has 93 subscribers.

You will learn about 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 Get the source code to implement the generalized Goertzel transform This course is ideal for individuals who are Data-science and financial market analysts interested in applying digital signal processing to analyzing and measuring cycles in financial markets or Experts who want to understand the differences between standard Fourier and Goertzel algorithm (FFT vs. G-DFT) It is particularly useful for Data-science and financial market analysts interested in applying digital signal processing to analyzing and measuring cycles in financial markets or Experts who want to understand the differences between standard Fourier and Goertzel algorithm (FFT vs. G-DFT).

Enroll now: Learn how to detect dominant cycles with spectrum analysis

Summary

Title: Learn how to detect dominant cycles with spectrum analysis

Price: $34.99

Average Rating: 3.95

Number of Lectures: 11

Number of Published Lectures: 11

Number of Curriculum Items: 11

Number of Published Curriculum Objects: 11

Original Price: $39.99

Quality Status: approved

Status: Live

What You Will 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
Get the source code to implement the generalized Goertzel transform

Who Should Attend

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)

Target Audiences

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)

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.
Get the source code to implement for your own usage

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.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction – Example dataset with 3 cycles

Lecture 2: Applying the Fast Fourier Transform “FFT” for cycle detection

Lecture 3: The Fourier index coefficient – time / frequency conversion

Chapter 2: Improving the Fast-Fourier-Transform

Lecture 1: Improving FFT resolution using "zero padding" (a)

Lecture 2: Improving FFT resolution: Using interpolation (b)

Lecture 3: Improving FFT resolution: Weighted average around cycle peaks (c)

Chapter 3: The Goertzel algorithm

Lecture 1: The Goertzel algorithm to detect cycles

Lecture 2: Generalized Goerzel algorithm to detect non-integer coefficients

Chapter 4: Comparison & Impacts FFT vs. Goertzel-DFT

Lecture 1: Results: Comparison FFT vs Goertzel cycle detection & error rates

Lecture 2: Impact: FFT vs generalized Goertzel error rate in projection area

Chapter 5: Source Code – generalized Goertzel Transform

Lecture 1: Source Code

Instructors

Lars von Thienen
DIGITAL SIGNAL PROCESSING, CYCLES DETECTION & FORECASTING

Rating Distribution

1 stars: 0 votes
2 stars: 0 votes
3 stars: 9 votes
4 stars: 3 votes
5 stars: 6 votes

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