Moving-Average Modeling Algorithm
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A Moving-Average Modeling Algorithm is a time series analysis algorithm that can be implemented by a moving-averable modeling system (to solve a moving-average modeling task).
- See: Stationary Process, Linear Prediction, Autoregressive Model, Autoregressive–Moving-Average Model, Autoregressive Integrated Moving Average, Moving Average.
References
2017
- (Wikipedia, 2017) ⇒ https://en.wikipedia.org/wiki/Moving-average_model Retrieved:2017-10-29.
- In time series analysis, the moving-average (MA) model is a common approach for modeling univariate time series. The moving-average model specifies that the output variable depends linearly on the current and various past values of a stochastic (imperfectly predictable) term.
Together with the autoregressive (AR) model, the moving-average model is a special case and key component of the more general ARMA and ARIMA models of time series, which have a more complicated stochastic structure.
The moving-average model should not be confused with the moving average, a distinct concept despite some similarities.
Contrary to the AR model, the finite MA model is always stationary.
- In time series analysis, the moving-average (MA) model is a common approach for modeling univariate time series. The moving-average model specifies that the output variable depends linearly on the current and various past values of a stochastic (imperfectly predictable) term.