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  "body": "\n## Summary\n\nAn autoregressive–moving-average model, or ARMA($p$, $q$) model, combines an [[autoregressive model|2JC96FMGA1]] of order $p$ with a [[moving-average model|PEB9DN0AHA]] of order $q$.\nEach value is a linear combination of $p$ past values plus the current shock and $q$ past shocks.\nARMA models describe a wide range of stationary time series with few parameters, and they are the core of the Box–Jenkins approach to forecasting.\n\n## Explanations\n\n### L1 human\n\nAn autoregressive part lets the series remember its own past values, and a moving-average part lets recent shocks echo for a few steps.\nUsing both parts often describes the data with fewer numbers than using either part alone.\nFor example, $X_t = 0.5 X_{t-1} + \\varepsilon_t + 0.3 \\varepsilon_{t-1}$ is an ARMA($1$, $1$) model.\nForecasts from it decay towards the mean at the rate $0.5$ per step, after a first step that also uses the last shock.\n",
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