Comparative analysis of neural network architectures for short-term FOREX forecasting

TL;DR

This paper compares various neural network architectures, including LSTM and a custom ANN, for short-term FOREX market prediction, highlighting the efficiency and accuracy of the custom ANN in resource-constrained environments.

Contribution

The study introduces a custom ANN architecture based on technical analysis indicators and benchmarks its performance against LSTM models for short-term FOREX forecasting.

Findings

The custom ANN outperforms LSTM architectures in prediction quality.

The custom ANN requires less computational resources and time.

LSTM architectures are more resource-intensive and slower.

Abstract

The present document delineates the analysis, design, implementation, and benchmarking of various neural network architectures within a short-term frequency prediction system for the foreign exchange market (FOREX). Our aim is to simulate the judgment of the human expert (technical analyst) using a system that responds promptly to changes in market conditions, thus enabling the optimization of short-term trading strategies. We designed and implemented a series of LSTM neural network architectures which are taken as input the exchange rate values and generate the short-term market trend forecasting signal and an ANN custom architecture based on technical analysis indicator simulators We performed a comparative analysis of the results and came to useful conclusions regarding the suitability of each architecture and the cost in terms of time and computational power to implement them. The ANN custom architecture produces better prediction quality with higher sensitivity using fewer resources and spending less time than LSTM architectures. The ANN custom architecture appears to be ideal for use in low-power computing systems and for use cases that need fast decisions with the least possible computational cost.