Cyclespatterns

Cyclespatterns is a term used in time series analysis to describe recurring, structured patterns in data that reflect cycles at different time scales. It encompasses strict periodic cycles, such as daily or seasonal effects, and quasi-periodic patterns where period or amplitude evolves over time. The concept emphasizes how multiple cycles interact within a single series and how these interactions affect forecasting and interpretation.

Key characteristics include multi-scale structure, amplitude and phase modulation, and nonstationarity. Cyclespatterns may arise from endogenous dynamics, external drivers, or sampling effects, and can exhibit regime changes that alter cycle properties.

Analysts detect cyclespatterns with time-frequency and decomposition methods. Fourier analysis suits stationary components, while wavelet analysis captures localized features. Other approaches include autocorrelation, spectral density estimation, and models that allow changing cycle parameters. Irregular data may require Lomb-Scargle periodograms.

Applications span economics (business and seasonal cycles), climatology, epidemiology, energy demand, and ecology. Understanding cyclespatterns supports forecasting, anomaly detection, and policy evaluation.

Limitations include nonstationarity, nonlinearity, regime shifts, and data gaps that complicate detection and interpretation. Analysts must guard against mistaking artifacts for genuine cycles.

Etymology and usage: cyclespatterns is a compound noun formed from "cycles" and "patterns" and appears mainly in informal data-science discussions. It is not a widely standardized term in formal literature.

See also: seasonality, periodicity, time-series analysis, Fourier analysis, wavelet transform.