Generating Financial Time Series by Matching Random Convolutional Features

By Konrad J. Mueller, Nikita Zozoulenko, Ben Wood, Thomas Cass, Lukas Gonon

Published 2026-06-03

Everscope rating

1653.7

Relevance to quantitative trading

9 / 10

Implementation complexity

5 / 10

Reproducibility

4 / 5

About this paper

Methodology: SOCK (SOftCompetingKernels) Feature Matching. Problem types: Generative Modeling, Time Series Forecasting, Risk Management, Classification, Density Estimation, Unsupervised Learning, Sequence-to-Sequence Learning.

arXiv:2606.05138 · Paper rankings

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