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@JSzitas
JSzitas
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Juraj Szitás JSzitas

Credit Quant DeveloperInterested in Optimization |Time Series Forecasting | Machine Learning | Trees
  • Barclays
  • Prague

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JSzitas/README.md

Hello! I’m Juraj Szitas.

I started out as an Econometrician, then I turned Data Scientist for a few years and eventually got sick of the AI hype. I currently work as a Quantitative Developer.My biggest passion (as you would probably be able to guess from here) is numerics, closely followed by anything time series related.

I keep my open source stuff here - in the hopes that someone finds it useful. It includes a lot of really neat little things that I either could not find elsewhere,or did not want to deal with the hassle of linking against/shipping with.

Check out:

  • nlsolver Nonlinear optimizers as header-only, C++17 library.
  • tinyqr A header-only, optimized C++17 implementation of the QR decomposition.
  • soothsayer if you like thefable framework and the idea of meta learning for time series, you might like this
  • blaze (WIP) A full fledged time series forecasting and analysis toolkit in modern C++.
    • contains a new (S)ARIMA(X) implementation leveraging SIMD
    • fully capable AR and AutoAR
    • Benchmark methods (Integrated Noise)
    • miscellaneous time series utility functions (seasonality identification, stationarity tests)
  • gpvolatility for an implementation of a funky volatility model

I mainly program C++/python these days, but I will do anything that's needed :) I have reached the point where getting things done in a timely manner matters most.

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  1. soothsayersoothsayerPublic

    Automatic Time Series Forecasting and Ensembling via Meta-learning

    R 1

  2. blazeblazePublic

    A C++17 implementation of ARIMA following R

    C++ 1 1

  3. nlsolvernlsolverPublic

    Easy, header only nonlinear optimizers in C++17

    C++ 2 1

  4. gpvolatilitygpvolatilityPublic

    A highly experimental R implementation of https://proceedings.neurips.cc/paper/2014/file/a733fa9b25f33689e2adbe72199f0e62-Paper.pdf

    MATLAB

  5. categoryEncodingscategoryEncodingsPublic

    Multiple methods to (quickly) encode factor variables, using data.table

    R 3 1


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