CONFIRMED: Data Science MS Thesis Defense by Balarama Krishna Padamata

CONFIRMED Wednesday, December 18, 2024 at 3:30pm to 4:30pm

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

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https://us05web.zoom.us/j/84455844861?pwd=re9AVgiuhYihYnA89CK7EMT0rboCAZ.1Meeting