Forecasting of FOREX Price Trend using Recurrent Neural Network - Long short-term memory

Abstract

Algorithms of neural networks (NN) can search and represent both structured and not structured data, we employ then on financial time-series. This paper describes the use of Long short-term memory (LSTM) for FOREX pair EUR/USD price prediction. Aim of the paper is to test and proposes the best time block to predict based on a daily FOREX data. We employ the mean of absolute errors and the least mean squared errors to assess prediction results in order to find the time block. We tested time blocks from ten to fifty-eight days and 100 or 300 epochs. Training dataset contained daily exchange rate data from 1.4.1971 until 9.5.2019. The best performing network has been trained for 30-day period and 100 epochs. This paper also describes the effect of training for a high number of epochs.