Data Science MS Thesis Defense by Balarama Krishna Padamata
When: Wednesday,
December 18, 2024
3:30 PM
-
4:30 PM
Where: > See description for location
Description: Optimizing Stock Market Prediction Using Advanced LSTM Based Architectures
By Balarama Krishna Padamata
Advisor: Gary Davis
Committee: Ashok Patel and Alfa Heryudono
Zoom Meeting Time: Dec 18, 2024 at 03:30 PM Eastern Time (US and Canada)
Join Zoom Meeting: https://us05web.zoom.us/j/84455844861?pwd=re9AVgiuhYihYnA89CK7EMT0rboCAZ.1Meeting
ID: 844 5584 4861
Passcode: UMASS1234
ABSTRACT:
Stock market prediction remains a challenging endeavor, given the volatile and complex nature of financial markets. In this research, we investigate the effectiveness of different Long Short-Term Memory (LSTM)-based neural network architectures, including LSTM, Bidirectional LSTM (BiLSTM), and CNN-LSTM, to forecast stock prices. We specifically focus on evaluating these models using various time frames of high-frequency stock data to understand which configuration yields the best predictive performance.
For additional information please contact Gary Davis at gdavis@umassd.edu
By Balarama Krishna Padamata
Advisor: Gary Davis
Committee: Ashok Patel and Alfa Heryudono
Zoom Meeting Time: Dec 18, 2024 at 03:30 PM Eastern Time (US and Canada)
Join Zoom Meeting: https://us05web.zoom.us/j/84455844861?pwd=re9AVgiuhYihYnA89CK7EMT0rboCAZ.1Meeting
ID: 844 5584 4861
Passcode: UMASS1234
ABSTRACT:
Stock market prediction remains a challenging endeavor, given the volatile and complex nature of financial markets. In this research, we investigate the effectiveness of different Long Short-Term Memory (LSTM)-based neural network architectures, including LSTM, Bidirectional LSTM (BiLSTM), and CNN-LSTM, to forecast stock prices. We specifically focus on evaluating these models using various time frames of high-frequency stock data to understand which configuration yields the best predictive performance.
For additional information please contact Gary Davis at gdavis@umassd.edu
Contact: > See Description for contact information
Topical Areas: Faculty, Staff and Administrators