LSTM Model

Long Short-Term Memory (LSTM) networks are a type of recurrent neural network designed to effectively process sequential data by addressing the vanishing gradient problem, enabling them to learn long-term dependencies. Current research focuses on applying LSTMs to diverse prediction tasks, including financial markets, environmental modeling, healthcare diagnostics, and natural language processing, often in hybrid architectures combining LSTMs with convolutional neural networks or transformers to improve accuracy and robustness. The widespread applicability of LSTMs across various domains highlights their significance in advancing machine learning capabilities and their potential for impactful real-world applications.