forecastcycli

forecastcycli is a conceptual framework for forecasting that emphasizes the explicit modeling of cyclical components in time series forecasts. The approach treats cycles—recurrent patterns with varying amplitude and period—as central elements alongside trend and irregular components.

Core ideas: Identify cycles using spectral analysis, time-domain cycle estimators, or state-space models with latent cycle states. Represent cycles with parametric forms (for example, sine and cosine terms) or nonparametric cycle components learned from data. Integrate cycle forecasts with other predictive components to produce final forecasts.

Methodology: 1) collect data, 2) detect cycles and estimate parameters (period, amplitude, phase), 3) construct a forecast model that includes cycle terms plus trend and irregular terms, 4) validate with out-of-sample tests and cross-validation, 5) monitor regime changes and adapt.

Applications: economic indicators, energy demand, weather-influenced variables, sales with business-cycle patterns, traffic flows, and other time series displaying recurrent fluctuations beyond simple seasonality.

Limitations: cycles may change over time, nonstationarity, regime shifts, data scarcity, risk of overfitting, computational complexity for complex cycle structures, and the need for careful model selection.

See also: time series decomposition, seasonal adjustment, Fourier analysis, Kalman filter, state-space models, cycle detection.