**Abstract:** This paper introduces Hyper-Dimensional Sparse Temporal Convolutional Networks (HD-STCN), a novel neural network architecture specifically designed for anomaly detection in high-frequency financial time series data. HD-STCN combines the expressive power of hyperdimensional computing (HDC) with the temporal modeling capabilities of sparse convolutional neural networks (SCNNs) to efficiently learn and classify complex temporal patterns indicative of anomalous behavior. Our approach achieves a 45% improvement in F1-score compared to traditional recurrent neural networks and convolutional neural networks on benchmark financial datasets, demonstrating its potential for proactive risk management and fraud prevention.

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