Revolutionizing Cardiac Muscle Detection. Harnessing the Power of Machine Learning

Bachelor Thesis , 2023 , 33 Pages

Medicine - Biomedical Engineering

This analysis explores advanced deep-learning techniques for stock price prediction, assessing transfer learning-based DTRSI, CNNs, and collaborative networks with sentiment analysis. DTRSI effectively addresses overfitting, outperforming traditional models. CNNs excel in predicting stock trends across time frames, while collaborative networks combining sentiment analysis and candlestick data show promise, particularly for specific stocks over longer periods. The study investigates the relevance of sentiment analysis from platforms like Twitter and StockTwits in predicting market movements. It introduces an innovative active deep learning approach for stock price forecasting, considering data size and sector impact. Emphasizing LSTM-based models, it highlights their potential to enhance stock price forecasting, offering insights for traders and investors by consolidating diverse prediction methods. This research lays the groundwork for future studies optimizing trading systems via data integration and advanced neural network architectures.

Inhaltsverzeichnis (Table of Contents)

• Chapter 1. INTRODUCTION
• Chapter 2. LITERATURE SURVEY
• Chapter 3. METHODOLOGY
• Chapter 4. RESULTS& DISCUSSION
• Chapter 5. CONCLUSION

Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)

This term paper explores the potential of deep learning techniques for stock price prediction, aiming to identify and evaluate the effectiveness of various models in capturing market trends and forecasting future stock values. The study delves into the application of different deep learning approaches, including transfer learning-based DTRSI, convolutional neural networks (CNNs), and collaborative networks integrated with sentiment analysis.

• Assessing the efficacy of deep learning techniques in stock price prediction.
• Evaluating the performance of DTRSI, CNNs, and collaborative networks.
• Analyzing the role of sentiment analysis in predicting market movements.
• Exploring the potential of active deep learning for stock price forecasting.
• Highlighting the capabilities of LSTM-based models for stock price prediction.

Zusammenfassung der Kapitel (Chapter Summaries)

Chapter 1 provides an introduction to the field of stock price prediction and outlines the rationale behind exploring deep learning techniques for this purpose. Chapter 2 conducts a comprehensive literature review, examining existing research on stock price prediction using deep learning, focusing on the application of DTRSI, CNNs, and collaborative networks. Chapter 3 delves into the methodology employed in the study, detailing the data collection, preprocessing, and model development processes. Chapter 4 presents and analyzes the results obtained from the implemented models, highlighting their strengths and limitations.

Schlüsselwörter (Keywords)

The primary keywords and focus topics of this term paper encompass: stock price prediction, deep learning techniques, DTRSI (Deep Transfer Reinforcement Stock Index), sentiment analysis, and LSTM-based models. These terms represent the core concepts and research focuses investigated in the study, highlighting the application of advanced computational methods for analyzing market trends and predicting future stock prices.

• Author: Yenni Rajasekhar (Author)
• Publication Year: 2023
• Pages: 33
• Catalog Number: V1437075
• ISBN (PDF): 9783346992611
• ISBN (Book): 9783346992628
• Language: English
• Tags: Stock price prediction, Deep learning techniques, DTRSI (Deep Transfer Reinforcement Stock Index), Sentiment analysis, LSTM-based models