When working with time series data, there are a number of important diagnostics one should consider to help understand more about the data. The auto-correlative function, plotted as a correlogram, helps explain how a given observations relates to recent preceding observations. A very random process (like lottery numbers) would show very low values, while temperature (our topic in this episode) does correlate highly with recent days. See the show notes with details about Chapel Hill, NC weather data by visiting: https://dataskeptic.com/blog/episodes/2016/acf-correlograms
![[MINI] Auto-correlative functions and correlograms - podcast episode cover](https://static.libsyn.com/p/assets/2/c/2/f/2c2f92aa368261b5/acf.png)
