Below I have attached two graphs of issues over time for a site in Chattanooga. (one is all issues over time, and the other is all issues restricted to date of the software update that causes the spikes) I am wondering what method of time series I could use to fit this data? I do not want to get rid of the "outliers"/spikes because this is what I am trying to fit to be able to "lay" the graph on another site location and make a prediction about its future spike. (therefore, I think smoothing the data or differencing defeats the purpose of what I am trying to do, but maybe I am misunderstanding) There are factors that go into play at Chattanooga and other sites, such as experience and size. Is this possible to incorporate and fit?
Any help is greatly appreciated!
Hi Charleigh,
One possible way to do this is to include dummy variables in your model. See for example : Modeling and Forecasting U.S. Mortality. R. D. Lee and L.R. Carter.
If this method will help you to fit the data, of course, it will not have a capacity of generalization to reproduce spikes in future.
Note that if you have two series (or more), you can use VAR method, and also add dummies if you want to do so.
Hope this helps,
Best regards !
I'm not 100% clear what you want, but maybe dynamic time warping is what you want. Allows you to match peaks between different time series by distorting the time axis. Easy to do in R. Google is your friend.
https://en.wikipedia.org/wiki/Dynamic_time_warping
Hi Charleigh,
if you want to do smoothing that prevents the spikes (at least to a parameterizable degree), maybe a Savitzky-Golay-Filter could be a suitable means for you.
Best regards!
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