Sequence Models

🔁 What Are Sequential Models?

Sequential models are a class of machine learning models designed to handle data where the order of elements matters. Unlike traditional models that treat each input independently, sequential models take into account the temporal or positional relationships between data points. These models are essential in domains where past information influences future predictions, such as time-series forecasting, natural language processing, speech recognition, and video analysis.

At the core of sequential modeling is the idea that data points are not isolated, but rather part of a structured progression. For example, in a sentence like “The cat sat on the mat,” the meaning of each word depends on the words that came before it. Similarly, in time series data such as temperature measurements or stock prices, the current value is often influenced by preceding values. Capturing this dependence is what differentiates sequential models from standard feedforward models, which assume that inputs are independent and identically distributed.

Traditional neural networks, like multilayer perceptrons (MLPs), expect fixed-size inputs and produce fixed-size outputs. They do not have a mechanism to retain memory or context across a sequence of inputs. As a result, they struggle with tasks involving variable-length sequences or long-term dependencies. To overcome this limitation, models like Recurrent Neural Networks (RNNs) were developed. RNNs introduce the concept of a hidden state that is updated at each time step, allowing information to be carried forward and influence future predictions.

However, basic RNNs are not without flaws — chiefly, their difficulty in learning long-term dependencies due to issues like vanishing and exploding gradients during training. To address these challenges, more sophisticated architectures such as Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRUs) were introduced. These models incorporate gating mechanisms to control the flow of information, enabling them to retain relevant information over longer sequences.

Sequential models are not limited to RNN-based architectures. Modern models like Transformers have revolutionized sequence modeling by using self-attention mechanisms instead of recurrence, enabling parallelization and improved performance on long sequences. Regardless of the specific architecture, the unifying principle remains the same: sequential models are built to respect and exploit the structure of ordered data, making them indispensable tools in modern machine learning.