Temporal Attention augmented Bilinear Network for Financial Time-Series Data Analysis (1712.00975v1)

Abstract: Financial time-series forecasting has long been a challenging problem because of the inherently noisy and stochastic nature of the market. In the High-Frequency Trading (HFT), forecasting for trading purposes is even a more challenging task since an automated inference system is required to be both accurate and fast. In this paper, we propose a neural network layer architecture that incorporates the idea of bilinear projection as well as an attention mechanism that enables the layer to detect and focus on crucial temporal information. The resulting network is highly interpretable, given its ability to highlight the importance and contribution of each temporal instance, thus allowing further analysis on the time instances of interest. Our experiments in a large-scale Limit Order Book (LOB) dataset show that a two-hidden-layer network utilizing our proposed layer outperforms by a large margin all existing state-of-the-art results coming from much deeper architectures while requiring far fewer computations.

• The paper introduces a novel bilinear network enhanced with a temporal attention mechanism to improve multivariate time-series forecasting.
• It demonstrates superior computational efficiency and prediction accuracy compared to traditional models like CNNs and LSTMs.
• The model enhances interpretability by pinpointing key temporal instances that drive the forecasting performance.

Temporal Attention Augmented Bilinear Network for Financial Time-Series Data Analysis

The paper "Temporal Attention Augmented Bilinear Network for Financial Time-Series Data Analysis" presents a novel approach to the complex problem of financial time-series forecasting, especially in the high-frequency trading (HFT) domain. This domain poses unique challenges due to the volatile and stochastic nature of financial markets, necessitating inference systems that are not only accurate but also computationally efficient to accommodate rapid prediction requirements.

Overview of the Proposed Approach

The authors introduce a neural network architecture featuring a bilinear layer augmented by a temporal attention mechanism. This layer is adept at identifying and emphasizing critical temporal information in sequential data, increasing interpretability by spotlighting the significance and influence of individual time instances on predictions. This approach is particularly geared towards multivariate time-series data, which is naturally represented as a second-order tensor, preserving the temporal infrastructure encoded within the data. Traditional vector-based models often fall short in capturing these temporal nuances, which makes the current method significantly advantageous.

Methodological Contributions

1. Bilinear Projection with Attention Mechanism: By integrating a bilinear mapping into the network layer, the proposed architecture separately learns dependencies along each mode of the multivariate input tensor. The attention mechanism further allows the model to assess the temporal importance of each instance, fostering a competitive dynamic among neurons representing identical features across different time steps.
2. Computational Efficiency: The theoretical analysis reveals the superior computational efficiency of the proposed temporal attention mechanism compared to traditional attention mechanisms used in recurrent neural networks like LSTM and GRU, which often incur substantial computational overhead, limiting their practicality.
3. Interpretability: The model’s design facilitates post-training analysis, providing insights into which temporal instances significantly impact model predictions. This feature holds potential for further investigations into temporal patterns or pseudo-periods within the data.

Experimental Results

The authors conducted experimental evaluations using the FI-2010 dataset, a substantial high-frequency Limit Order Book (LOB) dataset. The dataset contains over four million limit orders, making it a robust ground for testing. The proposed network architecture, even in shallow configurations with only two hidden layers, demonstrated superior performance compared to existing models comprising deeper architectures such as CNN and LSTM. The clear margin of improvement in prediction accuracy and computational requirements underscores the potential of this method for practical application in real-time high-frequency trading scenarios.

Implications and Future Directions

The integration of temporal attention in a bilinear framework opens new avenues for enhancing interpretability and efficiency in financial data analysis. From a practical standpoint, the decreased computational overhead alongside improved prediction accuracy positions the model as a valuable tool for traders operating in fast-paced environments. Theoretically, this advancement may provide insights into developing a broader class of models capable of capturing complex interdependencies inherent in multivariate time-series data.

Further research could explore extending this approach to other domains where multivariate time-series data analysis is crucial, such as in meteorology or healthcare. Additionally, embedding similar attention mechanisms in other tensor-based models could substantiate the benefits observed here across broader applications.

In conclusion, the paper makes significant strides in advancing the capabilities of neural network architectures to efficiently and effectively handle the intricacies of financial time-series forecasting, with promising implications for both theoretical exploration and practical application in high-frequency trading.