Study on Exchange Rate Forecasting with Stacked Optimization Based on a Learning Algorithm
Weiwei Xie,
Haifeng Wu,
Boyu Liu
et al.
Abstract:The time series of exchange rate fluctuations are characterized by non-stationary and nonlinear features, and forecasting using traditional linear or single-machine models can cause significant bias. Based on this, the authors propose the combination of the advantages of the EMD and LSTM models to reduce the complexity by analyzing and decomposing the time series and forming a new model, EMD-LSTM-SVR, with a stronger generalization ability. More than 30,000 units of data on the USD/CNY exchange rate opening pr… Show more
Set email alert for when this publication receives citations?
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.