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This paper presents Hyperdimensional Kernel Regression (HKR), a new method for forecasting stochastic time series in high-frequency financial markets by utilizing hyperdimensional computing and kernel regression techniques. HKR transforms financial data into hypervectors, significantly enhancing forecasting accuracy and speed compared to traditional models, particularly in volatile and noisy environments. The framework is commercially viable for quantitative trading and risk management, demonstrating superi

Author

freederia

Language

EN