Off-the-Shelf Neural Network Architectures for Forex Time Series Prediction come at a Cost
This work addresses the problem of efficient and accurate time series prediction for forex traders, but it is incremental as it compares existing methods without introducing new paradigms.
The study compared LSTM and specialized ANN architectures for forex market prediction, finding that the specialized ANN achieved better results with fewer resources and shorter execution times.
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.