Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/153503
Title: Financial time series data pattern detection, forecasting and its application
Authors: Ooi, Yuxuan
Keywords: Engineering::Computer science and engineering
Issue Date: 2021
Publisher: Nanyang Technological University
Source: Ooi, Y. (2021). Financial time series data pattern detection, forecasting and its application. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/153503
Abstract: This paper studies the latest techniques for financial time series forecasting by extending the existing work. In addition to historical stock data, sentiment analysis and signal analysis methods are applied to simulate the real-world factors that could potentially affect the stock trends. Three LSTM-based models with varied input features and architectures were trained and tested with different popular tech stocks. The experiment result shows that adding a new dimension of public sentiment helps to improve the prediction model to forecast a closing price trend that follows closely to the actual price. Furthermore, this paper proposes a trading platform that applies the prediction model built as a real-world use case. A trading algorithm is proposed to utilize the forecasted results to provide an auto-trading service and serves as the core service of the platform. The platform comes in the form of mobile application and is equipped with useful functionalities with the goal of capturing the market.
URI: https://hdl.handle.net/10356/153503
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

Files in This Item:
File Description SizeFormat 
Final Report_Ooi_Yuxuan_U1822211J.pdf
  Restricted Access
2.27 MBAdobe PDFView/Open

Page view(s)

77
Updated on May 19, 2022

Download(s) 50

24
Updated on May 19, 2022

Google ScholarTM

Check

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