Regression model
ARIMA (Autoregressive Integrated Moving Average) Model
ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015).
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Sources
- Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021
Related methods
Referenced by
Augmented Dickey-Fuller TestAutoformerBayesian Structural Time SeriesBreusch-Godfrey TestCointegration TestConditional Value-at-RiskConformal Prediction (Time Series)Croston's MethodDCC-GARCHDeepARDLinearEGARCHETS ModelExponential SmoothingExtreme Value TheoryGARCHGARCH ModelGJR-GARCHGM(1,1) Grey ForecastingHolt-WintersInformerJohansen Cointegration TestKalman Filter (Finance)KPSS TestLee-Carter ModelLjung-Box TestLong-Memory ModelsMarkov-Switching ModelMean-Variance Portfolio OptimizationMIDAS RegressionN-BEATSN-HiTSPatchTSTPhillips-Perron TestRealized VolatilitySARIMAXState Space ModelSTL DecompositionStructural Time Series ModelTBATSTemporal Fusion TransformerTheta MethodTime-Series Cross-ValidationValue at RiskVAR ModelVECMX-13ARIMA-SEATS