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m Amakuru moved page Autoregressive–moving-average model to Autoregressive moving-average model: Requested by Mechachleopteryx at WP:RM/TR: Easier to type and read, and more commonly used. |
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In order for the model to remain [[Stationary process|stationary]], the roots of its [[Autoregressive model#Characteristic polynomial|characteristic polynomial]] must lie outside of the unit circle. For example, processes in the AR(1) model with <math>|\varphi_1| \ge 1</math> are not stationary because the root of <math>1 - \varphi_1B = 0</math> lies within the unit circle.<ref>{{Cite book |last=Box |first=George E. P. |url=https://www.worldcat.org/oclc/28888762 |title=Time series analysis : forecasting and control |last2=Jenkins |first2=Gwilym M. |last3=Reinsel |first3=Gregory C. |date=1994 |publisher=Prentice Hall |others= |isbn=0-13-060774-6 |edition=3rd |___location=Englewood Cliffs, N.J. |pages=54-55 |language=en |oclc=28888762}}</ref>
ADF assesses the stability of IMF and trend components. For stationary time series, the Autoregressive Moving Average (ARMA) model is used, while for non-stationary series, LSTM models are employed to derive abstract features. The final value is obtained by reconstructing the predicted outcomes of each time series.
== Moving-average model ==
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