Lesson plan

Moving-average model

In time series analysis, the moving-average model (MA model), also called the moving-average process, is a standard approach for modeling univariate time series. An MA model expresses the current value of a time series as a linear function of current and past random shocks (error terms) with finite lag length. In contrast to an autoregressive model, which regresses the variable on its past values, the moving-average model relies solely on the dependency structure of the error terms. 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. Contrary to the AR model, the finite MA model is always stationary. The moving-average model should not be confused with the moving average, a distinct concept despite some similarities.

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Article summary

In time series analysis, the moving-average model (MA model), also called the moving-average process, is a standard approach for modeling univariate time series. An MA model expresses the current value of a time series as a linear function of current and past random shocks (error terms) with finite lag length. In contrast to an autoregressive model, which regresses the variable on its past values, the moving-average model relies solely on the dependency structure of the error terms. 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. Contrary to the AR model, the finite MA model is always stationary. The moving-average model should not be confused with the moving average, a distinct concept despite some similarities.

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  1. Summarise the article in your own words. Which single claim would you verify first, and why?
  2. Which claim in this article is most likely to be revised as the sources update? How would you check its current status?
  3. If two sources disagreed about one of these facts, what would you need in order to trust one over the other?

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