Applications of Deep Learning to Financial Time Series Forecasting
Name
camelosa-jlucas16-meng-eecs-2023-thesis.pdf
Description
Thesis PDF
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332.52 KB
Format
Adobe PDF
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e5d58e5dd2a195efb20ac215ab245148
Author(s)
Camelo Sa, Lucas
Advisor(s)
Kim, Yoon
Date Issued
June 2023
Publisher
Massachusetts Institute of Technology
Abstract
Deep learning has recently risen as a dominant technique in a variety of settings comprising large-scale and high-dimensional data. In the particular case of financial modeling, one of the most important data analysis problems consists of predicting the future volatility of a given asset. In this thesis, we investigate how the Transformer architecture performs at the task of volatility forecasting by comparing its performance against that of previously explored deep learning architectures such as the LSTM.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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