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You can definitely improve your prediction based on new data, but the most important part is to show your historical predictions too. FiveThirtyEight does this

You can definitely improve your prediction based on new data, but the most important part is to show your historical predictions too. FiveThirtyEight does this pretty well by showing the probability a candidate gets elected in a time series chart. Predicting is serious business, and you have to be accountable for past predictions, especially if they were wrong. This chart, as is is extremely misleading because it gives readers the false sense that you were 100% right, and your future predictions are highly likely to be true. >> Extrapolating out into the future is always errorprone. You're absolutely right. And if readers saw your predictions were mostly wrong, they wouldn't be alarmed in the future when they see "experts" predict 10% of the world would die 3 months after a virus is found. Because they'd know from past experience that past models were mostly wrong!

mlyle 7y ago
> This chart, as is is extremely misleading because it gives readers the false sense that you were 100% right, and your future predictions are highly likely to be true. Instead, if readers saw your past predictions were mostly wrong, they wouldn't have this false perception. Just hit the button to toggle to the log scale version of it. A exponential fit is appropriate and past predictions using an exponential fit would be good. :P Do you understand how to fit data series? My fifth grader could hold up a ruler-- align it the the beginning of the yellow line and each subsequent point and see how reasonable of a prediction they are of future points. He'd say "Yup, it's pretty straight!" And even for the red line, though a little less so. Yah, it would be cool if they'd tell us r^2. > And if readers saw your predictions were mostly wrong, they wouldn't be alarmed in the future when they see "experts" predict 10% of the world would die 3 months after a virus is found. Note that exponential models predict everyone will get the disease and everyone will die if you extrapolate out far enough. (Actually, -more- than everyone heh). Obviously we don't think that's the case. But it's a pretty damn reasonable way to see 3 days forward in the early phases of an epidemic.
- vackosar 7y ago
  agreed. I mention there that this is simplistic. I may look into the sigmoid shape s-curve once it is time for it. If the site is still interesting to somebody at that time.

mlyle 7y ago

> This chart, as is is extremely misleading because it gives readers the false sense that you were 100% right, and your future predictions are highly likely to be true. Instead, if readers saw your past predictions were mostly wrong, they wouldn't have this false perception. Just hit the button to toggle to the log scale version of it. A exponential fit is appropriate and past predictions using an exponential fit would be good. :P Do you understand how to fit data series? My fifth grader could hold up a ruler-- align it the the beginning of the yellow line and each subsequent point and see how reasonable of a prediction they are of future points. He'd say "Yup, it's pretty straight!" And even for the red line, though a little less so. Yah, it would be cool if they'd tell us r^2. > And if readers saw your predictions were mostly wrong, they wouldn't be alarmed in the future when they see "experts" predict 10% of the world would die 3 months after a virus is found. Note that exponential models predict everyone will get the disease and everyone will die if you extrapolate out far enough. (Actually, -more- than everyone heh). Obviously we don't think that's the case. But it's a pretty damn reasonable way to see 3 days forward in the early phases of an epidemic.

vackosar 7y ago
agreed. I mention there that this is simplistic. I may look into the sigmoid shape s-curve once it is time for it. If the site is still interesting to somebody at that time.