Year: 2024
Type: article
Abstract: Stock price prediction is a critical and challenging task in the field of financial analysis and investment decision making. The power of artificial intelligence and machine learning is leveraged to offer more reliable predictions for investors and financial institutions. This study explores and implements various machine learning architectures, including tree-based models such as decision trees, random forests and neural network based models like Recurrent Neural Networks (RNNs), LSTM (Long short-term memory) networks, GRU(Gated Recurrent Unit) and LSTM-CNN hybrid. The implementation of the above mentioned machine learning algorithms are assessed across various comparison criteria, which include accuracy, robustness, generalization and computational efficiency
Author: Amogh Thakurdesai
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4404005801