trenddelen

Trenddelen is a term used in data analysis to describe the partitioning of a time series into distinct trend segments. The aim is to identify intervals where the data move in a coherent direction or hold a stable level, and to mark where these trends change. It emphasizes change points and segment-level behavior rather than general smoothing.

Etymology and scope: The word appears mainly in Dutch-language technical writing and in discussions of time-series segmentation. It is often used alongside terms like segmented trend analysis or trend segmentation, but it is not part of a canonical statistical taxonomy. The concept is compatible with several modeling frameworks that support piecewise behavior.

Methods commonly used for trenddelen include breakpoint detection and piecewise linear regression, modeling the series as consecutive linear segments joined at breakpoints. Algorithms such as dynamic programming and information-criterion-based model selection (for example BIC) help determine the number and location of segments. Bayesian and hidden Markov approaches can also infer regime shifts.

Applications include finance, for identifying regime changes in returns; climate science, for detecting shifts in warming or cooling trends; and epidemiology, for assessing changes in disease incidence trends. The approach supports interpretability by providing explicit temporal boundaries between trend regimes.

Limitations include sensitivity to noise, the risk of overfitting with too many segments, and the challenge of choosing the number of segments. Validation and uncertainty reporting are important to avoid spurious breakpoints, and results should be presented with confidence measures where possible.