Wavelet Analysis: Applications with Wolfram Language
51mAdvanced2024-01-10
Authors
Wolfram Research
Course details
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.
Skills covered
Wolfram LanguageWolfram ResearchComputational DesignData AnalysisAECProduct and ManufacturingData ScienceBusiness Analysis and StrategyBusiness Software and ToolsOne-Off
Concepts
0. Introduction
- 01 - Introduction - Wavelet Analysis
1. Frequency Detection in a Time Series
- 02 - Frequency Detection in a Time Series
2. Filtering Frequencies from Time Series
- 03 - Filtering Frequencies from Time Series
3. Applying Wavelet Analysis to Finance
- 04 - Applying Wavelet Analysis to Finance
4. Edge Detection on Images
- 05 - Edge Detection on Images
5. Denoising Images
- 06 - Denoising Images
6. Wavelet Thresholding
- 07 - Wavelet Thresholding
References
- 08 - References