ARIMA & smoothing
31 methods in this family.
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ARIMAARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series froARIMA modelThe ARIMA(p,d,q) model is the standard workhorse for univariate time series forecasting. It combines autoregressive terms (past values), differencing to induce stationarity, and moARMA modelThe ARMA(p,q) model describes a stationary time series as a combination of two components: an autoregressive part that regresses the current value on its own past p values, and a mETS ModelETS is a comprehensive exponential smoothing framework that automatically selects additive or multiplicative combinations of the error (E), trend (T) and seasonal (S) components ofETSformerETSformer is a deep learning architecture for time-series forecasting introduced by Woo et al. in 2022. It integrates classical exponential smoothing principles directly into the TExponential SmoothingExponential smoothing is a family of basic time-series forecasting models in which each new observation updates a smoothed estimate by a weighting parameter. Simple exponential smo
All methods 31
ARIMAARIMA modelARMA modelETS ModelETSformerExponential SmoothingFourier ARIMA modelFourier ARMA modelFourier SARIMA modelHolt-WintersMoving Average ModelMultilevel Power AnalysisNonlinear ARIMA modelNonlinear ARMA modelNonlinear SARIMA ModelPanel ARIMA modelPanel ARMA modelPanel SARIMA modelRobust ARIMA modelRobust ARMA ModelRobust SARIMA modelRobust Time Series AnalysisSARIMASARIMA modelSARIMAXStructural Break ARIMA ModelStructural Break SARIMA ModelTime-varying parameter ARIMA modelTime-varying parameter ARMA modelTime-varying parameter SARIMA modelX-13ARIMA-SEATS