  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.
