Udemy - Learn how to detect dominant cycles with spectrum analysis

  • CategoryOther
  • TypeTutorials
  • LanguageEnglish
  • Total size729.4 MB
  • Uploaded Byfreecoursewb
  • Downloads37
  • Last checkedApr. 19th '26
  • Date uploadedApr. 18th '26
  • Seeders 7
  • Leechers7

Infohash : 8ACFE0C5A67F5E2C43C5B7D5F38B263BB44BF36A

Learn how to detect dominant cycles with spectrum analysis

https://WebToolTip.com

Last updated 11/2023
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 49m | Size: 729.4 MB

Using the Fast Fourier Transform and the DFT-Goertzel algorithm to detect cycles in noisy data sets (financial markets)

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
Get the source code to implement the generalized Goertzel transform

Requirements
Basic cycle and/or spectrum analysis knowledge is helpfull, but not mandatory.

Files:

[ WebToolTip.com ] Udemy - Learn how to detect dominant cycles with spectrum analysis
  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here ! 1 - Introduction
    • 1. 1_ExampleDataset.pdf (356.7 KB)
    • 1. Example dataset with 3-cycles.url (0.1 KB)
    • 1. Introduction - Example dataset with 3 cycles (Description).html (1.1 KB)
    • 1. Introduction - Example dataset with 3 cycles.en_US.srt (1.7 KB)
    • 1. Introduction - Example dataset with 3 cycles.mp4 (24.6 MB)
    • 1. Raw test signal (N=800).url (0.1 KB)
    • 2. 2_Applying_FFT.pdf (252.4 KB)
    • 2. Applying the Fast Fourier Transform “FFT” for cycle detection (Description).html (1.2 KB)
    • 2. Applying the Fast Fourier Transform “FFT” for cycle detection.en_US.srt (12.3 KB)
    • 2. Applying the Fast Fourier Transform “FFT” for cycle detection.mp4 (185.8 MB)
    • 2. FFT Spectrum.url (0.1 KB)
    • 3. 3_Fourier_Indexpdf.pdf (222.4 KB)
    • 3. The Fourier index coefficient – time frequency conversion (Description).html (1.5 KB)
    • 3. The Fourier index coefficient – time frequency conversion.en_US.srt (1.0 KB)
    • 3. The Fourier index coefficient – time frequency conversion.mp4 (9.7 MB)
    • 3. k-th Frequency Coefficient vs Cycle Length (for dataset N=800).url (0.1 KB)
    2 - Improving the Fast-Fourier-Transform
    • 4. 4_Improving_ZeroPadding.pdf (242.0 KB)
    • 4. Improving FFT resolution using zero padding (a) (Description).html (1.1 KB)
    • 4. Improving FFT resolution using zero padding (a).en_US.srt (4.6 KB)
    • 4. Improving FFT resolution using zero padding (a).mp4 (58.3 MB)
    • 5. 5_Improving_Interpolaton.pdf (183.4 KB)
    • 5. Improving FFT resolution Using interpolation (b) (Description).html (1.0 KB)
    • 5. Improving FFT resolution Using interpolation (b).en_US.srt (6.3 KB)
    • 5. Improving FFT resolution Using interpolation (b).mp4 (113.0 MB)
    • 6. 6_Improving_WeightedAverage.pdf (676.2 KB)
    • 6. Improving FFT resolution Weighted average around cycle peaks (c) (Description).html (1.1 KB)
    • 6. Improving FFT resolution Weighted average around cycle peaks (c).en_US.srt (1.5 KB)
    • 6. Improving FFT resolution Weighted average around cycle peaks (c).mp4 (19.9 MB)
    3 - The Goertzel algorithm
    • 7. 7_Goertzel.pdf (202.8 KB)
    • 7. The Goertzel algorithm to detect cycles (Description).html (1.6 KB)
    • 7. The Goertzel algorithm to detect cycles.en_US.srt (3.6 KB)
    • 7. The Goertzel algorithm to detect cycles.mp4 (73.6 MB)
    • 8. 8_Generalized_Goertzel.pdf (410.4 KB)
    • 8. Generalized Goerzel algorithm to detect non-integer coefficients (Description).html (2.0 KB)
    • 8. Generalized Goerzel algorithm to detect non-integer coefficients.en_US.srt (6.0 KB)
    • 8. Generalized Goerzel algorithm to detect non-integer coefficients.mp4 (110.6 MB)
    • 8. Goertzel algorithm generalized to non-integral multiples of fundamental frequency (MATLAB).url (0.1 KB)
    • 8. Goertzel_Article_Algorithm.pdf (459.8 KB)
    • 8. Sysel, P., Rajmic, P. Goertzel algorithm generalized to non-integer multiples of fundamental frequency. EURASIP J. Adv. Signal Process. 2012, 56 (2012).url (0.1 KB)
    4 - Comparison & Impacts FFT vs. Goertzel-DFT
    • 10. (Zoom) Impact of 2% vs. 0.16% error rate in cycle projection window.url (0.1 KB)
    • 10. 10_Impact_ErrorRates.pdf (455.3 KB)
    • 10. Impact FFT vs generalized Goertzel error rate in projection area (Description).html (0.8 KB)
    • 10. Impact FFT vs generalized Goertzel error rate in projection area.en_US.srt (3.3 KB)
    • 10. Impact FFT vs generalized Goertzel error rate in projection area.mp4 (51.9 MB)
    • 10. Impact of 2% vs. 0.16% error rate in cycle projection window.url (0.1 KB)
    • 9. 9_Results.pdf (129.7 KB)
    • 9. Results Comparison FFT vs Goertzel cycle detection & error rates (Description).html (0.8 KB)
    • 9. Results Comparison FFT vs Goertzel cycle detection & error rates.en_US.srt (2.6 KB)
    • 9. Results Comparison FFT vs Goertzel cycle detection & error rates.mp4 (26.3 MB)
    5 - Source Code - generalized Goertzel Transform
    • 11. Gen_Goertzel.pdf (111.4 KB)
    • 11. Generalized_Goertzel_Transform.txt (2.9 KB)
    • 11. Source Code (Description).html (0.7 KB)
    • 11. Source Code.en_US.srt (6.1 KB)
    • 11. Source Code.mp4 (52.0 MB)
    • Bonus Resources.txt (0.1 KB)

Code:

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