## 3 Prior Works  
In the exploration of related works, we delve into the existing literature and research with the aim of identifying the state-of-the-art model for intraday stock price prediction using limit order book (LOB) data and using it as a benchmark for comparison with the model proposed in this work.  
As discussed in [11], extensive research has been dedicated to predicting stock price movements using diverse data sources and modelling techniques. Earlier studies exhibited a wide range of variations in datasets, experimental setups, and metrics for assessing model performance, rendering them nearly incomparable. Additionally, the poor reproducibility of many experiments stemmed from the limited public availability of datasets and code. A pivotal improvement occurred with the introduction of the first public benchmark limit order book (LOB) dataset in 2017 [27]. This initiative provided a shared foundation for research in the field, facilitating greater standardisation in experimental setups and performance metrics, complementing the benchmark LOB dataset itself. Consequently, the review of prior works focuses on models trained specifically on this benchmark LOB dataset. The most notable among them are summarised in Table 3.  
As evident from Table 3, the performance metrics, including F1 score, accuracy, precision, and recall, exhibited gradual enhancements as progressively more complex models were applied. For the fundamental linear and nonlinear classification models, namely Ridge Regression (RR) [27] and Support Vector Machine (SVM) [16], the F1 score hovered  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Archetype** & **Model** & **Ref.** & **Date** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline Linear Classification & RR & [27] & 09/2017 & 48.00 & 41.80 & 43.50 & 41.00 \\ \hline Nonlinear Classification & SVM & [16] & 09/2017 & - & 39.62 & 44.92 & 35.88 \\ \hline Multi-linear Classification & MTR & [28] & 12/2017 & 86.08 & 51.68 & 40.81 & 40.14 \\  & WMTR & [28] & 12/2017 & 81.89 & 46.25 & 51.29 & 47.87 \\ \hline Image Classification & BoF & [28] & 12/2017 & 57.59 & 39.26 & 51.44 & 36.28 \\ \hline \multirow{3}{*}{Dimensionality Reduction} & LDA & [28] & 12/2017 & 63.82 & 37.93 & 45.80 & 36.28 \\  & MDA & [29] & 10/2017 & 71.92 & 44.21 & 60.07 & 46.06 \\  & MCSDA & [29] & 10/2017 & 83.66 & 46.11 & 48.00 & 46.72 \\ \hline Neural Network & MLP & [16] & 09/2017 & - & 47.81 & 60.78 & 48.27 \\ \hline \multirow{9}{*}{Deep Learning} & N-BoF & [28] & 12/2017 & 62.70 & 42.28 & 61.41 & 41.63 \\  & CNN-LSTM & [17] & 08/2020 & - & 56.00 & 45.00 & 44.00 \\  & HeMLGOP & [30] & 03/2019 & 83.06 & 48.57 & 50.67 & 49.43 \\  & CNN & [13] & 06/2017 & - & 50.98 & 65.54 & 55.21 \\  & LSTM & [16] & 09/2017 & - & 60.77 & 75.92 & 66.33 \\  & DAIN-MLP & [31] & 02/2019 & - & 65.67 & 71.58 & 68.26 \\  & aTABL-IS1 & [32] & 04/2023 & 80.31 & 73.13 & 66.26 & 69.73 \\  & aTABL-IS2 & [32] & 04/2023 & 80.56 & 75.80 & 66.47 & 70.00 \\  & CTABL & [33] & 09/2018 & 84.70 & 76.95 & 78.44 & 77.63 \\  & BiN-C(TABL) & [34] & 03/2020 & 86.87 & 80.29 & 81.84 & 81.04 \\  & DeepLOB & [10] & 03/2019 & 84.47 & 84.00 & 84.47 & 83.40 \\  & TransLOB & [35] & 02/2020 & 87.66 & 91.81 & 87.66 & 88.66 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance of state-of-the-art stock price prediction models on the benchmark LOB dataset.  
around 40 percent. The introduction of shallow neural network architectures, such as Multilayer Perceptron (MLP) [16], elevated the F1 score to nearly 50 percent. Further increases in the F1 score to around 70 percent were achieved with deep learning models like Long Short-Term Memory (LSTM) [16]. The best performance among those deep learning models was demonstrated by the DeepLOB [10] and TransLOB architectures [35]. According to their respective authors, each of them demonstrated accuracy and F1 scores well in excess of 80%. However, since the reproducible code was available only for DeepLOB [10], it was chosen as a benchmark for comparison with the proposed model.