Список проголосовавших за комментарий "BeyG, а это Вы внимательно прочли:<br/> <blockquote>In this section we discuss the proposed LiT model architecture (<a id="F2a" class="ArticleReference" href="/r.php?u=https%3A%2F%2Fwww.frontiersin.org%2Fjournals%2Fartificial-intelligence%2Farticles%2F10.3389%2Ffrai.2025.1616485%2Ffull%23F2&s=1742366672">Figure 2</a>) which consists of three main components: (1) a linear projection concatenated with positional embeddings to efficiently represent structured patches from the limit order book data; (2) transformer layers utilizing self-attention mechanisms to encode spatial and temporal dependencies between patches; and (3) LSTM layers to further model long-term temporal dependencies. Additionally, we provide details regarding the experimental training and fine-tuning settings.<br/> <p>4.1.1 Input layers<br/> Drawing inspiration from multi-channel representations in image processing (e.g., RGB channels), the grid-like structure of limit order book data is represented using two input channels: one for price and one for volume information. This results in a three-dimensional input x∈ℝH×W×C (Figure 2), where H denotes the depth of the LOB (i.e., the number of price levels), W is the window size representing the number of time steps used to construct each training example, and C is the number of channels which in this case is 2 for price and volume channels.</p></blockquote>"