I'm currently working on a project researching whether Google Trends can predict conflict events in intrastate conflicts.
Thus I have two different datasets; the weekly Google Trends search volume and the number of conflict events per week. My idea was to do the following to test my hypothesis:
Use an outbreak detection algorithm from the R-package
surveillance. This will give me binomial values (outbreak/no outbreak) for each week in my datasets. The idea was that those algorithms would be able to correctly and automatically identify "peaks" in my data.
Evaluate the binomial classifiers (so if there was an "outbreak" in the Google Trends data for week t and there is an "outbreak" in the conflict data in week t+1 I'd have a true positive etc.).
I am, however not totally sure, which algorithm provided in the package would be suitable for my kind of data (which I assume does not follow seasonal trends as strongly as disease data), since my background is not in epidemiology and my knowledge of statistics is quite limited. I would thus be thankful for hints or advice!