Cycleseg

Cycleseg is a conceptual framework and, in some contexts, a software package for segmenting cyclic time-series data into discrete cycles or phases. The aim is to identify boundaries between cycles where statistical properties shift and to align data to a common reference cycle when possible.

As a methodological approach, cycleseg combines cycle alignment, phase estimation, and boundary detection. It commonly relies on models that assume repeating patterns, uses change-point detection to locate transitions, and may apply smoothing or normalization to reduce within-cycle noise. Extensions include probabilistic models such as hidden Markov models and optimization methods based on dynamic programming or Bayesian inference.

Implementation typically involves components such as a cycle detector, a boundary scorer, and an evaluator. Outputs include a segmentation mask or cycle-labeled sequence and statistics describing within- and between-cycle variability. The approach is designed to work with standard data formats and to integrate with numerical computing libraries.

Applications span audio and speech processing for beat or phrase segmentation, biomedical signal analysis for cardiac or respiratory cycles, industrial monitoring of periodic machine operations, and environmental data analysis for seasonal cycle delineation.

Limitations include sensitivity to irregular or nonstationary cycles, which may require robust modeling and careful preprocessing. The choice of reference cycle, priors, and model assumptions can influence segmentation results, and validation often depends on domain-specific metrics.

See also: time-series segmentation, changepoint detection, cyclic analysis.