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Effectiveness Of Cnn-Lstm Models Used For Apple Stock Forecasting, Ethan White
Effectiveness Of Cnn-Lstm Models Used For Apple Stock Forecasting, Ethan White
Electronic Theses, Projects, and Dissertations
This culminating experience project investigates the effectiveness of convolutional neural networks mixed with long short-term memory (CNN-LSTM) models, and an ensemble method, extreme gradient boosting (XGBoost), in predicting closing stock prices. This quantitative analysis utilizes recent AAPL stock data from the NASDAQ index. The chosen research questions (RQs) are: RQ1. What are the optimal hyperparameters for CNN-LSTM models in stock price forecasting? RQ2. What is the best architecture for CNN-LSTM models in this context? RQ3. How can ensemble techniques like XGBoost effectively enhance the predictions of CNN-LSTM models for stock price forecasting?
The research questions were answered through a thorough …