Off-the-Shelf Neural Network Architectures for Forex Time Series Prediction come at a Cost

arXiv:2405.10679v1 Announce Type: new
Abstract: 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 …

abstract analyze ann architecture architectures arxiv cost cs.ai cs.ce cs.lg cs.pf long short-term memory lstm market memory network neural network performance prediction requirements resources series study time series type