Error in the Google model is by definition the difference from the CDC levels.
It also fails fairly spectacularly on a local level when compared to health department data from some top flight state agencies, like NY.
There are three datasets here: the CDC's, Google's, and reality. Some folks here seem unable to differentiate between the CDC data and reality. But in fact, I really did have the flu, even though CDC didn't think I did. Reality is what matters.
Maybe there is reason to believe that CDC's data is closer to reality than Google's. If so, let's hear it.
The CDC is unapologetic about their data being estimates - but they're very solid estimates, and match more intensive but smaller scale studies pretty well. But we don't need to capture every single flu case - no surveillance system will ever do that, nor need to.
Edit: I see how the CDC's numbers could be a lower bound, but not upper.
[edit: For example, the author of the paper could have compared Google and CDC versus a third data source, like sales of cold medicine. That would have been an interesting comparison, and might have shed some light on which estimates were a better reflection of reality.]
Things like "sales of cold medicine" and the like fall under the heading of "syndromic surveillance", and believe me, they've been tried for the better part of a decade without yielding much that's impressive. Which is frustrating, because theoretically they should be amazing, but they just aren't, especially when asked to predict rather than back fit.
The CDC numbers (which are part of ILINet) are pretty decent, and one of the things they do, which hasn't been mentioned here, is they're not trying to find all cases of influenza. They have a network of physicians in all 50 states reporting "Influenza-like Illness". These people then also send samples in for laboratory confirmation of virus type, drug resistance, etc. The numbers are also backwards revised as more data comes in, meaning they improve over the course of the year.
It's a very good system for getting an idea of how "bad" the flu season is - the main problem is that it's slow. The ideal, and what people were hoping from Google Flu Trends (and other systems like it, it's not alone) is "ILINet, but in real time".
Then perhaps you didn't have strictly the flu, but some other viral upper respiratory tract infection instead.
It is hard to judge from symptoms alone (which is why lab confirmations are required for diagnosis), but as a general rule of thumb influenza A infections in adults tend to be much more severe and long lasting than what you describe [1], with distinctively higher malaise, fever and incapacitation than your regular winter cold. It is so debilitating that you probably would have gone to the doctor in any case, I would guess.
I have never gone to the doctor when I had a flu. If it had lasted more than one or two days, or if I had a temperature higher than 102, then I would have gone. But as it was, nobody ever knew that I had the flu except myself and my family.
Even if I had wanted to go to the doctor for every time I got a flu or a cold, it takes at least a day or two to schedule a doctor's appointment. By the time the appointment had rolled around I would probably have cancelled, since the flu doesn't usually last very long.
This being the US, there are also a lot of uninsured people who can't go to the doctor even if they wanted to. For those people, Google is the only option. The CDC can collect as much data as it wants from the laboratories, but you can't collect what isn't there.
When you add in the uninsured people and the people who get better before visiting the doctor, it's not at all surprising that the lab estimates are 2x lower than reality. In fact, I would kind of expect them to be even lower than that.