Date: Fri, 22 Apr 2016 15:00:00 +0000
<div id="bbody"> <div class="container" id="notebook-container"> <div class="cell border-box-sizing text_cell rendered"> <div class="prompt input_prompt">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.</div> </div> <div class="cell border-box-sizing code_cell rendered"> <div class="input"> <div class="prompt input_prompt"> </div> <div class="prompt input_prompt">See the show notes with details about Chapel Hill, NC weather data by visiting:</div> <div class="prompt input_prompt"> </div> <div class="prompt input_prompt"><a href="https://dataskeptic.com/blog/episodes/2016/acf-correlograms">https://dataskeptic.com/blog/episodes/2016/acf-correlograms</a></div> <div class="prompt input_prompt"> </div> </div> </div> </div> </div>
