What type of data is a recurrent neural network (RNN) best suited to process?

Recurrent Neural Networks (RNNs) are specifically designed to handle sequential data, making them particularly well-suited for tasks involving time-series data, natural language processing, and other scenarios where the order and context of the data points matter. Unlike traditional neural networks that treat input data as independent and identically distributed, RNNs have connections that loop back on themselves, enabling them to maintain a 'memory' of previous inputs. This feature allows RNNs to capture patterns over time, making them ideal for processing sequences where the relationship between data points is dependent on their order.

For instance, in natural language processing, RNNs can effectively track the context of words in a sentence to understand meaning, where the sequence of words contributes significantly to interpretation and understanding. Thus, when the question pertains to the type of data best suited for RNNs, sequential data stands out as the correct answer due to its inherent dependency features that RNNs are designed to exploit.