Introduces SOCK (SOft Competing Kernels), a differentiable random convolutional feature map, to train generative models of financial time series via feature matching and shows outperformance over signature and diffusion baselines on small-sample datasets.
Sig-wasserstein gans for time series generation
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Semi-supervised Bayesian GANs with log-signatures for uncertainty-aware credit card fraud detection show consistent improvements over benchmarks on the BankSim simulator under varying label proportions.
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Generating Financial Time Series by Matching Random Convolutional Features
Introduces SOCK (SOft Competing Kernels), a differentiable random convolutional feature map, to train generative models of financial time series via feature matching and shows outperformance over signature and diffusion baselines on small-sample datasets.
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Semi-Supervised Bayesian GANs with Log-Signatures for Uncertainty-Aware Credit Card Fraud Detection
Semi-supervised Bayesian GANs with log-signatures for uncertainty-aware credit card fraud detection show consistent improvements over benchmarks on the BankSim simulator under varying label proportions.