Smoothing Crypto Time Series with Wavelets - Real-world Data Project

Explore a real-world data science project focused on smoothing cryptocurrency time series using wavelets. Learn about various techniques for analyzing and processing financial time series data, including moving averages, polynomial fitting, Fourier transforms, and wavelet decomposition. Gain insights into the practical application of data science concepts in the cryptocurrency market. Follow along as the instructor demonstrates each method, comparing their effectiveness and discussing their implications. Discover additional resources for deeper understanding of time series analysis, wavelet transforms, and Fourier transforms. By the end of this tutorial, grasp the power of data science in solving real-world problems and gain valuable experience in applying these techniques to financial data.

Overview -
Background -
Initial Thoughts -
Baseline Solution: Moving Average -
Solution 1: Polynomial Fit -
Solution 2: Fourier Transform-
Solution 3: Wavelet Decomposition -
The Power of Data Science -