Rapid large-scale fractional differencing with NVIDIA RAPIDS and GPU to minimize memory loss while making a time series stationary. 6x-400x speed up over CPU implementation.
☆57Oct 4, 2019Updated 6 years ago
Alternatives and similar repositories for fractional_differencing_gpu
Users that are interested in fractional_differencing_gpu are comparing it to the libraries listed below. We may earn a commission when you buy through links labeled 'Ad' on this page.
Sorting:
- Implementing features from "Advances in Financial Machine Learning" by Marcos López del Prado in a financial algorithm using Enigma Catal…☆11Jul 13, 2020Updated 5 years ago
- Machine learning trading method using meta-labeling. You can see the details in 'Advances in Financial Machine Learning' by Lopez de Prad…☆15Jul 20, 2021Updated 4 years ago
- Advancing in Financial Machine Learning☆16Feb 27, 2020Updated 6 years ago
- Interoperability between Polars and Clickhouse☆14Feb 26, 2025Updated last year
- Implementation of the [Hierarchical (Sig-Wasserstein) GAN] algorithm for large dimensional Time Series Generation: https://doi.org/10.390…☆17Nov 28, 2022Updated 3 years ago
- End-to-end encrypted cloud storage - Proton Drive • AdSpecial offer: 40% Off Yearly / 80% Off First Month. Protect your most important files, photos, and documents from prying eyes.
- ☆26Mar 8, 2019Updated 7 years ago
- MFM workshop project☆15Jan 25, 2021Updated 5 years ago
- ☆15Oct 7, 2019Updated 6 years ago
- [Recurrent Weighted Average](https://arxiv.org/abs/1703.01253) [Jared Ostmeyer et.al, 2017] heavily based on https://gist.github.com/sham…☆10May 18, 2017Updated 8 years ago
- Sorting libraries for pyculib