An Introduction to Deep Learning for Sequential Data

Highlighting the similarities between time series and NLP

How I imagine myself when I load a time series dataset. Image by the author. (AI assisted)

Sequential data like time series and natural language require models that can capture ordering and context. While time series analysis focuses on forecasting based on temporal patterns, natural language processing aims to extract semantic meaning from word sequences.

Though distinct tasks, both data types have long-range dependencies where distant elements influence predictions. As deep learning has advanced, model architectures initially developed for one domain have been adapted to the other.

Sequential data

Time series and natural language have both a sequential structure, where the position of an observation in the sequence matters greatly.

A time series is a sequence of values. (left) Text is a sequence of words. (right) Image by author.

A time series is a set of observations over time that are ordered chronologically and sampled at fixed time intervals. Some examples include:

• Stock prices every day
• Server metrics every hour
• Temperature readings every second