Generating Financial Time Series by Matching Random Convolutional Features
- Published
- Source
- arXiv
- Paper number
- 318
- Field
- Machine Learning
- arXiv ID
- 2606.05138
Key points
- To mitigate this, recent approaches train generators to minimize the mismatch between non-learned feature representations of real and generated time series.
- This paper shows that a generator trained by matching random SOCK features consistently outperforms signature-based and diffusion-based baselines across multiple small-sample financial datasets.
- The authors further demonstrate SOCK's expressiveness on two-sample hypothesis testing and time-series classification tasks, where SOCK matches or surpasses existing unsupervised feature maps.
Paper links
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