Forecasting of Forex Time Series Data Based on Deep Learning

Lina Ni · Yujie Li · Xiao Wang · Jinquan Zhang · Jiguo Yu · Chengming Qi

122 citations0/0 files storedProcedia Computer Science

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Stock Market Forecasting Methods Forecasting Techniques and Applications Time Series Analysis and Forecasting

Abstract

This paper proposes a C-RNN forecasting method for Forex time series data based on deep-Recurrent Neural Network (RNN) and deep Convolutional Neural Network (CNN), which can further improve the prediction accuracy of deep learning algorithm for the time series data of exchange rate. We fully exploit the spatio-temporal characteristics of forex time series data based on the data-driven method. On the exchange rate data of nine major foreign exchange currencies, the experimental comparison of the forecasting method shows that the C-RNN foreign exchange time series data prediction method constructed in this paper has better applicability and higher accuracy.

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2015Forecasting exchange rate using deep belief networks and conjugate gradient method

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