Our study focuses on comparing the performance and resource requirements
between different Long Short-Term Memory (LSTM) neural network architectures
and an ANN specialized architecture for forex market prediction. We analyze the
execution time of the models as well as the resources consumed, such as memory
and computational power. Our aim is to demonstrate that the specialized
architecture not only achieves better results in forex market prediction but
also executes using fewer resources and in a shorter time frame compared to
LSTM architectures. This comparative analysis will provide significant insights
into the suitability of these two types of architectures for time series
prediction in the forex market environment.

本研究旨在比较不同的长短期记忆（LSTM）神经网络结构和专门用于外汇市场预测的 ANN 架构之间的性能和资源需求，包括模型的执行时间、内存和计算资源消耗。我们的目标是证明专门的架构在外汇市场预测方面不仅能够取得更好的结果，而且在资源消耗和执行时间方面相对于 LSTM 架构更为优越。这种比较分析将对这两种类型的架构在外汇市场环境中的时间序列预测的适用性提供重要的见解。