FreqAI: generalizing adaptive modeling for chaotic time-series market forecasts

Python Submitted 11 October 2022 • Published 15 December 2022
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Authors

Robert A. Caulk (0000-0001-5618-8629), Elin Törnquist (0000-0003-3289-8604), Matthias Voppichler, Andrew R. Lawless, Ryan McMullan, Wagner Costa Santos, Timothy C. Pogue, Johan van der Vlugt, Stefan P. Gehring, Pascal Schmidt (0000-0001-9328-4345)

Citation

Caulk et al., (2022). FreqAI: generalizing adaptive modeling for chaotic time-series market forecasts. Journal of Open Source Software, 7(80), 4864, https://doi.org/10.21105/joss.04864

@article{Caulk2022, doi = {10.21105/joss.04864}, url = {https://doi.org/10.21105/joss.04864}, year = {2022}, publisher = {The Open Journal}, volume = {7}, number = {80}, pages = {4864}, author = {Robert A. Caulk and Elin Törnquist and Matthias Voppichler and Andrew R. Lawless and Ryan McMullan and Wagner Costa Santos and Timothy C. Pogue and Johan van der Vlugt and Stefan P. Gehring and Pascal Schmidt}, title = {FreqAI: generalizing adaptive modeling for chaotic time-series market forecasts}, journal = {Journal of Open Source Software} }
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Machine Learning adaptive modeling chaotic systems time-series forecasting

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ISSN 2475-9066