Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/72562
Title: Exchange rate prediction based on machine learning methods
Authors: Hong, Yuyue
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2017
Abstract: Actually, exchange rate is a kind of important data in economy. There is immense economic information which can be found through exchange rate. Actually, many researchers have used several methods and done experiments to forecast the exchange rate. Machine learning is popular and can be used in many fields. A common use of it is doing some forecasting works in time-series because it can be used to deal with regression problems. Neural Network is an efficient machine learning method to solve forecasting problems. Random Vector Functional Link Network, at the same time, also shows good performance for solving the forecasting problems. In addition, Random Forest and Support Vector Regression also perform well. In this paper, we use machine learning methods mentioned above to forecast the exchange rate between five different currencies and the United States dollar. Keywords: Exchange Rate, Time Series Forecasting, Machine Learning, Neural Networks, Random Forest, Support Vector Regression, Random Vector Functional Link Network
URI: http://hdl.handle.net/10356/72562
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

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