Sequential data exhibits specific properties that distinguish it from the independent data points often assumed in simpler machine learning models. These unique characteristics necessitate special handling, and understanding them is main to appreciating why architectures like Recurrent Neural Networks (RNNs) are necessary and effective.
Perhaps the most defining characteristic of sequential data is that the order of elements is significant. Unlike a collection of features describing a house (where the order you list square footage, number of bedrooms, and location doesn't change the house itself), changing the order in a sequence often drastically alters its meaning or the pattern it represents.
Consider these two sentences:
The words are identical, but the order conveys entirely different scenarios. Similarly, shuffling the daily prices of a stock would render the data useless for predicting future trends, as the progression over time is lost. Standard feedforward neural networks, which process inputs without regard to their position in a sequence, inherently struggle with this property. They lack the mechanism to understand that the input at step should be interpreted in the context of inputs at steps , , and so on.
Closely related to order is the concept of temporal dependencies. This means that elements in a sequence are often related to, or dependent on, elements that came before (and sometimes after) them. The strength and range of these dependencies can vary greatly.
Simple RNNs, as we will see later, can struggle to maintain information over very long sequences, a problem often referred to as the vanishing gradient problem. This difficulty in learning long-range dependencies motivated the development of more advanced architectures like LSTMs and GRUs.
Here's a simple visualization of a time series exhibiting temporal dependency. The value at any given point seems related to its preceding values.
A sequence where each point's value appears influenced by previous points.
Unlike structured datasets where each sample typically has a fixed number of features (e.g., columns in a table), sequences often have variable lengths. Sentences can be short or long, time series measurements might cover different durations, and audio clips vary in length.
This poses practical challenges for machine learning models, especially when processing data in batches. Standard deep learning frameworks often expect input tensors with uniform shapes within a batch. If you have sequences of lengths 5, 10, and 7, how do you combine them efficiently for parallel processing on GPUs?
Common techniques to address this include:
We will cover these techniques in detail in Chapter 8 when discussing data preparation.
These three characteristics, order importance, temporal dependencies, and variable length, are central to understanding sequential data. They necessitate models capable of processing inputs incrementally, maintaining memory (state) across steps, and handling differing sequence lengths gracefully. This sets the stage perfectly for introducing Recurrent Neural Networks in the next chapter.
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