کتاب Deep Learning Models for Forecasting Financial Markets and Blockchain Validation of Transactions

Chapter 1 4
Theoretical and Conceptual Foundations 4
Evolution of Deep Learning in Data Science 4
Structure and Dynamics of Financial Markets 7
Fundamentals of Blockchain Technology and Data Integrity 11
Integration of Artificial Intelligence with Decentralized Finance 15
Opportunities and Challenges in AI-Driven Financial Systems 20
Chapter 2 26
Core Architectures of Deep Learning 26
Artificial Neural Networks and Training Mechanisms 26
Convolutional Neural Networks (CNNs) for Financial Data Analysis 30
Recurrent Models (RNN, LSTM, GRU) for Time Series Forecasting 34
Attention Mechanisms and Transformer Models for Market Prediction 38
Chapter 3 44
Financial Data Processing and Pre-Modeling Techniques 44
Data Collection and Construction of Financial Databases 44
Data Cleaning, Normalization, and Noise Reduction 49
Feature Engineering for Financial and Macroeconomic Variables 54
Integration of Unstructured Data: News, Text, and Social Media 59
Designing Input Pipelines for Multi-Source Predictive Models 64
Chapter 4 70
Deep Learning Models for Market Forecasting 70
LSTM-Based Models for Stock Price and Index Prediction 70
Hybrid Architectures for Volatility and Risk Forecasting 73
Fusion of Statistical and Deep Models for Fundamental Analysis 79
Reinforcement Learning for Algorithmic Trading Decisions 82
Evaluation Metrics: Profitability, Accuracy, and Robustness 86
References 89