You've completely missed my point. You had to go to a 4th order polynomial to get a regression with a decent fit. Look at your own graph, the curve constantly changes (gasp - 3 times! polynomial 101!). Try getting rid of that third upswing in the data and then calculating a 3rd order regression on the truncated data set, then projecting the future using the regression. You'll see that your model has no predictive qualities, you'll be off by a million miles. Now think what adding one more 'point' is going to change? Absolutely nothing.
Linear regression works well, do a quadratic if the data looks exponential, but once you start getting into 3rd and 4th order regressions without knowing exactly what the hell you are doing and why, it's just stupid. Learn to math, friend!
edit: I don't mean to be rude, after having this stuff drilled into my head for years it pains me to see bad math. And using higher-order polynomials just so your model fits past data better with the benefits of hindsight is just bad math. The cumulative data looks exponential, use a quadratic and call it a day if you want to be genuine. But it will still be a bad model because Rob Ford and the news cycle isn't predictable.