ARMA MODEL Meaning and
Definition
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The ARMA model, which stands for Autoregressive Moving Average model, is a statistical technique used in time series analysis to predict future values based on the past behavior of a given variable. It combines two fundamental components, namely autoregressive (AR) and moving average (MA), to capture both the linear dependence on past observations and the stochastic fluctuations in the data.
In an ARMA model, the autoregressive component represents the relationship between the variable and its own past values. It assumes that the current value of the variable can be predicted based on a linear combination of its previous values weighted by autoregressive coefficients.
On the other hand, the moving average component accounts for the random shocks or errors that influence the variable's behavior. It is constructed by taking the weighted average of the past errors with weights determined by moving average coefficients.
Combining these two components, the ARMA model is expressed as an equation, where the current value of the variable is a function of its own lagged values and past errors. The model parameters, including the lag order and coefficients, are estimated using various statistical techniques like maximum likelihood estimation.
By modeling the underlying patterns and capturing the dynamics of the data, ARMA models are widely used for time series forecasting and analyzing economic, financial, and environmental data. They provide a flexible framework to understand the behavior and make predictions based on the available information.
Common Misspellings for ARMA MODEL
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Etymology of ARMA MODEL
The word "ARMA model" is not associated with a specific etymology as it is an acronym derived from the terms "AutoRegressive Moving Average". ARMA models are a class of statistical models used in time series analysis and forecasting. The term was coined by the mathematician George E. P. Box and colleagues in the early 1970s. The "AR" component represents the autoregressive part, which refers to the linear dependence of the dependent variable on its own past values. The "MA" component represents the moving average part, which refers to the linear dependence of the dependent variable on past white noise or error terms. Together, the ARMA model combines these two components to capture both the autoregressive and moving average properties of a time series.
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