Which architecture is designed to handle long-term dependencies in data?

Long-Short-Term Memory (LSTM) is specifically designed to manage and learn from long-term dependencies in sequences of data, making it an ideal architecture for tasks that require understanding of context over extended time frames. Traditional neural networks suffer from limitations in capturing these long-range dependencies due to issues like vanishing and exploding gradients. LSTMs address this problem through a unique cell structure that includes mechanisms, such as forget gates, input gates, and output gates, allowing the network to maintain and utilize information over long intervals effectively.

In contrast, Convolutional Neural Networks (CNNs) are primarily used for spatial data, such as images, and excel at detecting patterns and features in those types of data rather than temporal relationships. Support Vector Machines (SVM) focus on classification and regression tasks by finding the optimal hyperplane separating different classes, which does not inherently manage dependencies over time. Linear Regression is a basic statistical method used for determining relationships between variables but does not deal with sequence data or dependencies at all. Therefore, LSTM is the most suitable architecture for handling long-term dependencies in data.