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    This study introduces a novel neural network layer for financial time-series forecasting. The new architecture improves accuracy and speed in high-frequency trading, outperforming existing methods with fewer computations.

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    Area of Science:

    • Quantitative Finance
    • Machine Learning
    • Time-Series Analysis

    Background:

    • Financial time-series forecasting is complex due to market noise and stochasticity.
    • High-frequency trading demands accurate and fast automated inference systems.
    • Existing deep architectures often require significant computational resources.

    Purpose of the Study:

    • To propose a novel neural network layer architecture for financial time-series forecasting.
    • To enhance accuracy and computational efficiency in high-frequency trading.
    • To develop an interpretable model that highlights crucial temporal information.

    Main Methods:

    • Developed a neural network layer incorporating bilinear projection and an attention mechanism.
    • Designed the layer to detect and focus on critical temporal information.
    • Utilized a two-hidden-layer network for experiments on limit order book data.

    Main Results:

    • The proposed two-hidden-layer network significantly outperformed state-of-the-art results.
    • The architecture achieved superior performance compared to much deeper networks.
    • The model required substantially fewer computations than existing methods.

    Conclusions:

    • The novel neural network layer offers a highly accurate and computationally efficient solution for financial forecasting.
    • The interpretability of the model aids in analyzing important temporal instances.
    • This approach advances automated inference systems for high-frequency trading.