Longitudinal analysis

advanced

Examine how labels, states, and analytical trajectories change across repeated observations.

Overview

Longitudinal analysis extends a labeling result across repeated observations belonging to the same entity.

It transforms sequences of analytical states into transition, timing, dwell, movement, and trajectory-level information while preserving their relationship to the parent run.

Dataset requirements

An entity identifier
Repeated observations per entity
An observation index or observation datetime
A compatible parent labeling result

Analytical outputs

Label sequences
Start and terminal state profiles
Transition profiles
Jump counts
Temporal profiles
Dwell profiles where datetime information is available
Movement and directionality information
Trajectory-level representations

Supported analytical contexts

Classification

Follow discrete supervised states across repeated observations.

Regression

Represent continuous outcomes as finite states when trajectory analysis requires categorical transitions.

Clustering

Follow unsupervised labels through repeated observations in feature or embedded analytical space.

Interpretation

Longitudinal results inherit assumptions and uncertainty from the parent labeling model.
Observed state changes should not automatically be interpreted as causal or clinically meaningful transitions.
Temporal interpretation depends on the quality and meaning of observation ordering or datetime information.