{"id":171,"date":"2022-01-29T22:01:03","date_gmt":"2022-01-29T22:01:03","guid":{"rendered":"http:\/\/aurimas.eu\/blog\/?p=171"},"modified":"2022-07-23T04:58:03","modified_gmt":"2022-07-23T04:58:03","slug":"an-interactive-introduction-to-wavelets-and-discrete-wavelet-transformation-for-data-scientists","status":"publish","type":"post","link":"https:\/\/aurimas.eu\/blog\/2022\/01\/an-interactive-introduction-to-wavelets-and-discrete-wavelet-transformation-for-data-scientists\/","title":{"rendered":"An interactive introduction to wavelets and discrete wavelet transformation for data scientists"},"content":{"rendered":"\n

A few weeks ago, our team won Georgia Tech’s MSc Analytics case competition week sponsored by COX communications. In it, we were asked to build a solution to detect impaired cable transmission signal frequencies, cluster such signals based on similarity of impairments and, leveraging information about the physical network structure, identify potential upstream sources of impairment.<\/p>\n\n\n\n

One of the reasons we were able to get to a winning solution was the fact that I happened to learn about this magical thing called discrete wavelet transformation in one of my classes in the fall (ISYE6404 Nonparametric Statistics, in case you’re wondering). Why magical? Well, I don’t know how else to describe an approach that allows you to take a ~8700 data points long signal and approximate it with a small set of coefficients (perhaps as few as 100-1000) while keeping approximation error below ~5%. <\/p>\n\n\n\n

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Original signal<\/figcaption><\/figure>\n\n\n\n
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Reconstructed signal that achieves 95% compression rate with only 3.5% error rate.<\/figcaption><\/figure>\n\n\n\n

When I first learned about them, I looked for resources online but a lot of them were too advanced to fully grasp. One exception to the rule was a blog post A Guide to Using the Wavelet Transform in Machine Learning<\/a> – I highly recommend reading it through. <\/p>\n\n\n\n

In any way, I figured I will create something myself, too. The case competition provided an excellent dataset to build on, and The Practical Guide to Discrete Wavelet Transformation<\/a> was born. I hope you find it useful!<\/p>\n\n\n\n

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