How do recurrent neural networks handle sequential data?

Recurrent Neural Networks (RNNs) handle sequential data by maintaining a hidden state that captures information from previous steps in a sequence. Unlike feedforward neural networks, which process each input independently, RNNs use loops to pass information from one step to the next. This allows them to process sequences of arbitrary length while retaining context. For example, when analyzing a sentence, an RNN processes one word at a time, updating its hidden state to reflect the meaning of the sentence up to that point. The hidden state acts as a memory that influences how the network interprets subsequent inputs.

The key mechanism in RNNs is the repeated application of the same set of parameters across all time steps. At each step, the network takes two inputs: the current data point (e.g., a word in a sentence) and the hidden state from the previous step. These inputs are combined using weights and activation functions to produce a new hidden state and an output. For instance, in a time series forecasting task, an RNN might take hourly temperature readings. At each hour, it uses the current temperature and the hidden state (which summarizes past temperatures) to predict the next hour’s value. This parameter sharing makes RNNs efficient for sequences, as the same logic is reused regardless of the sequence’s length.

However, standard RNNs struggle with long-term dependencies due to the vanishing or exploding gradient problem during training. For example, in a paragraph of text, an RNN might forget key details from the first sentence by the time it reaches the end. To address this, variants like Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRUs) were developed, which use gates to control information flow. Despite these limitations, basic RNNs are still useful for shorter sequences or tasks where context is local, such as simple language modeling or real-time sensor data processing. Their ability to handle sequential data without fixed input sizes makes them foundational for tasks like speech recognition, machine translation, and time series analysis.