Which type of neural network is primarily used for sequential data analysis such as time series forecasting?

Recurrent Neural Networks (RNNs) are primarily designed for processing sequential data and are particularly effective for tasks that involve temporal dependencies, such as time series forecasting. RNNs possess a unique architecture that includes loops within their hidden layers, allowing them to maintain a form of memory about previous inputs while processing a sequence. This capability is essential when the current data point is influenced by prior information, which is common in time-dependent datasets.

In applications like time series forecasting, RNNs can learn and model patterns over time, making them suitable for predicting future values based on past observations. The architecture enables them to perform well in scenarios with varying time intervals and sequences of different lengths.

Other types of neural networks may excel in different contexts. For instance, Convolutional Neural Networks (CNNs) are primarily utilized for image-related tasks due to their strong performance in spatial hierarchies and are not designed to inherently process sequences. Feedforward Neural Networks do not retain any information about previous inputs, making them less effective for sequential data analysis. Generative Adversarial Networks (GANs) are primarily used for generating new data similar to a training dataset and are not specifically tailored for sequential tasks. Hence, the RNN's design and functionality make it