Procedia Computer Science · 2019 · 120 citations · 28 references
ComputingComputer ScienceFinanceForecastingMachine LearningTime Series AnalysisDeep LearningFinancial Time Series AnalysisForeign Exchange MarketsTime Series ForecastingTime Series ModelingTime Series PredictionConvolutional Neural NetworksCurrency ForecastingDeep Learning For ForecastingDeep Learning For Time Series ForecastingDeep Learning-based ForecastingNeural ForecastingNeural Time Series ForecastingSpatio-temporal Forecasting
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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