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  "body": "\n## Summary\n\nThe autocorrelation function of a weakly [[stationary process|DT0QXJE705]] gives, for each lag $h$, the correlation between $X_t$ and $X_{t+h}$.\nIt summarizes the linear dependence in a [[time series|EA3MF4VTEH]] in one function of the lag.\nIts sample version is the first tool for identifying a time series model.\n\n## Explanations\n\n### L1 human\n\nTake a time series and lay a copy of it next to the original, shifted by $h$ steps.\nThe autocorrelation at lag $h$ measures how well the shifted copy lines up with the original, on a scale from $-1$ to $1$.\nFor daily temperatures, the autocorrelation at lag $1$ is high, because today is usually like yesterday.\nFor independent coin tosses, the autocorrelation is $0$ at every lag $h \\ne 0$.\n",
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