What type of neural network is specifically designed for sequence prediction?

Recurrent Neural Networks (RNNs) are specifically designed for sequence prediction due to their unique architecture that allows them to process sequences of data while maintaining a form of memory. In RNNs, connections between nodes form directed cycles, which enable the network to take into account previous inputs and outputs when predicting the next value in a sequence. This characteristic makes RNNs particularly suitable for tasks where context and order matter, such as time series forecasting, natural language processing, and speech recognition.

The design of RNNs allows them to keep track of previous inputs through hidden states, which can be updated with new information while still retaining knowledge of earlier inputs. This is in contrast to other types of neural networks, which typically process data in a static manner without the ability to maintain context over time.

In comparison, Convolutional Neural Networks (CNNs) excel at tasks involving spatial data, such as image classification, and Feedforward Neural Networks process input data in a straightforward manner without any recurrent connections, making them less suitable for sequence-related tasks. Generative Adversarial Networks (GANs) consist of two neural networks competing against each other for the generation of new data, which also does not align with the requirements of sequence prediction. Therefore, R