Abstract:
We'll examine an innovative approach using an optimized algorithm to create a decision tree for the basis of regime dependent and pattern classification of financial and macroeconomic time-series data. Implemented in a supervised and unsupervised learning framework, the algorithm relies on the GPU for high performance computing and the host processor to further integrate the results in a deep learning framework. Also, we implement random number generation, in part, using a hardware quantum based true random number generator, balanced with the pseudo-random number generator in CUDA, so as to optimize overall speed where an exhaustive search is not feasible.
We'll examine an innovative approach using an optimized algorithm to create a decision tree for the basis of regime dependent and pattern classification of financial and macroeconomic time-series data. Implemented in a supervised and unsupervised learning framework, the algorithm relies on the GPU for high performance computing and the host processor to further integrate the results in a deep learning framework. Also, we implement random number generation, in part, using a hardware quantum based true random number generator, balanced with the pseudo-random number generator in CUDA, so as to optimize overall speed where an exhaustive search is not feasible.
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Topics:
Finance, Accelerated Data Science, Artificial Intelligence and Deep Learning, Algorithms & Numerical Techniques
Event:
GTC Silicon Valley