Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/139217
Title: Foreign exchange prediction and trading using long-short-term-memory neural network
Authors: Tan, Shyer Bin
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2020
Publisher: Nanyang Technological University
Project: A3261-191
Abstract: Foreign exchange (forex) market is the largest financial market in the world. The relevance of this market results in many researches carried out to study its operation. In the past few years, many techniques have been developed to study, including technical analysis, where researchers tried to predict the forex rate based on past forex data. Neural network is a tool for predicting value based on training data. In the context of forex prediction, the past trading data is used as training data. The sheer size of trading volume means good amount of data for time series analysis and prediction. Researchers try to explore the intrinsic relationships in trading data, understand the pattern of fluctuation of the forex rate, and make use of these models to build accurate models to predict future rates. A signal decomposition method will also be used to reduce the complexity of the highly liquid forex data. In this project, the work done by published research papers will first be replicated. This is to set up a standard for comparison for different predicting models, using same set of data and comparable parameters for the neural networks. Next, the focus of the project will be improving the performance of prediction, where Long-Short-Term Memory Neural Network (LSTM) and Variational Mode Decomposition (VMD) technique are used to predict the forex rate.
URI: https://hdl.handle.net/10356/139217
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

Files in This Item:
File Description SizeFormat 
FYP_Final_Report.pdf
  Restricted Access
1.34 MBAdobe PDFView/Open

Page view(s)

323
Updated on Feb 2, 2023

Download(s) 50

27
Updated on Feb 2, 2023

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.