Which of the following statements is true about the RNN model?

The statement that RNNs can capture time dependencies in data is accurate because Recurrent Neural Networks (RNNs) are specifically designed to process sequences of data. Unlike traditional neural networks, which treat inputs independently, RNNs maintain a hidden state that allows them to retain information about previous inputs in the sequence. This capability makes them particularly effective for tasks where the order and temporal relationships of the data points are important, such as in natural language processing, speech recognition, and time series forecasting.

The ability of RNNs to remember previous inputs and use that context to inform future outputs is crucial when dealing with sequences, allowing them to model patterns over time. This makes them well-suited for applications where understanding the flow of time or sequence matters, unlike other models that do not inherently capture such dependencies.