Wavelet Analysis: Applications with Wolfram Language

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Wolfram Research

51:36

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01 - Introduction

01 - Introduction Wavelet Analysis.mp4

06:37

02 - 1. Frequency Detection in a Time Series

01 - Frequency Detection in a Time Series.mp4

07:20

03 - 2. Filtering Frequencies from Time Series

01 - Filtering Frequencies from Time Series.mp4

04:34

04 - 3. Applying Wavelet Analysis to Finance

01 - Applying Wavelet Analysis to Finance.mp4

02:50

05 - 4. Edge Detection on Images

01 - Edge Detection on Images.mp4

09:17

06 - 5. Denoising Images

01 - Denoising Images.mp4

04:03

07 - 6. Wavelet Thresholding

01 - Wavelet Thresholding.mp4

16:22

08 - References

01 - References.mp4

00:33

Description

This course presents examples from a variety of wavelet analysis applications in the Wolfram Language, including financial time series, edge detection and denoising of images, thresholding, image and data compression, and image fusion. Familiarity with Fourier transforms and data smoothing methods is recommended for this class. Learn to analyze a time series using wavelets for detecting discontinuities, isolating peaks and inspecting nonstationary behavior; apply wavelet analysis to financial data; detect edges and discontinuities in images and other two-dimensional data; reduce noise in images by removing higher-frequency components; and more.

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