Clustering in Longitudinal Data

A Focus on Short Time Series with R Example

Photo by Shlomo Shalev on Unsplash

Longitudinal data, characterized by repeated observations of the same subjects over time, plays a pivotal role in various fields such as economics, medicine, and social sciences. Unlike cross-sectional data, which captures a snapshot at a single point in time, longitudinal data provides a dynamic view, allowing researchers to observe changes, identify trends, and understand patterns across different time intervals. This type of data is invaluable for longitudinal studies aiming to assess the effects of time-dependent variables on specific outcomes.

In the realm of data analysis, clustering emerges as a fundamental technique for uncovering the inherent structures within data. Clustering in longitudinal data, and more specifically in short time series, involves grouping entities based on the similarity of their trajectories over time. Short time series, often consisting of limited data points, present a unique set of challenges. They require careful handling to accurately capture the temporal dynamics and patterns that might be less apparent than in longer series.

The concept of clustering short time series is crucial for several reasons. It enables the identification of common patterns among subjects, facilitates the prediction of future behaviors based on these…