Fractional Difference to Minimize Memory Loss While Making a Time Series Stationary

(Last Updated On: October 12, 2019)
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Here is an article with math stuff in it with time series. This has been a while since I sent something out. I am not sure how this will help. Maybe you can figure out it out by sending me your opinion.

Here is a summary of the article:

In this post, we covered fractional differencing, in particular the fixed window fractional differencing variant where we can achieve stationarity in our time series while minimizing information loss. On time series with millions of data points, fractional differencing can be computationally expensive, hindering rapid experimentation or real-time deployment. We show, through RAPIDS cuDF and numba, we can achieve 100x to 10000x speed-up depending with links to various implementations from easy (100x speed-up) to more complex (10000x speed-up).

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Thanks Bryan

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About caustic

Hi i there My name is Bryan Downing. I am part of a company called QuantLabs.Net This is specifically a company with a high profile blog about technology, trading, financial, investment, quant, etc. It posts things on how to do job interviews with large companies like Morgan Stanley, Bloomberg, Citibank, and IBM. It also posts different unique tips and tricks on Java, C++, or C programming. It posts about different techniques in learning about Matlab and building models or strategies. There is a lot here if you are into venturing into the financial world like quant or technical analysis. It also discusses the future generation of trading and programming Specialties: C++, Java, C#, Matlab, quant, models, strategies, technical analysis, linux, windows P.S. I have been known to be the worst typist. Do not be offended by it as I like to bang stuff out and put priorty of what I do over typing. Maybe one day I can get a full time copy editor to help out. Do note I prefer videos as they are much easier to produce so check out my many video at