concurrent.futures to speed up predict

prediction code into Stan centainly will have a huge speedup!

I make a predict() which takes about 5s, and these 3 sub-function takes about:

predict_trend() # less than 1 s
predict_seasonal_components() # about 2s
predict_uncertainty()   # about 3s

Since predict_seasonal_components() takes about 40% time, parallel execuing will sppedup about 40% comparing with in order executing whatever prediction code into Stan or not~

With Python3’s concurrent.futures module, writing parallel executing code seems very easy-- just a few lines?

_Originally posted by @leafonsword in https://github.com/facebook/prophet/issues/479#issuecomment-380656086_

[bletham]

Forecast time will depend directly on two things: the number of points for which you are making forecasts (that is, the number of rows in the future dataframe), and the parameter uncertainty_samples.

For the first, make sure that you are only making forecasts at points that you actually need forecasts for. For instance, by default the make_future_dataframe function will include all of the points in the history. If you don’t need predictions at all of the points in the history, then you can leave them out and there will be fewer predictions that need to be made.

The parameter uncertainty_samples determines the number of Monte Carlo simulations done to estimate future trend uncertainty. It is by default 1000, to estimate a 80% interval. If you are willing to have a bit more variance in your estimates of the uncertainty, you could reduce that to 100 when you instantiate the Prophet model, like m = Prophet(uncertainty_samples=100). This would make prediction much faster (~5x?). It will not effect the main forecast estimate yhat, but will make yhat_lower and yhat_upper a bit noisier.

[bletham]

How many rows are in future?

If you don’t need uncertainty estimation, then you can now disable that with uncertainty_samples=0 which makes things much faster. The latest version (v0.6) also includes #1311 which speeds up predict. Also be sure that future includes only the dates you actually care about (i.e. you may not need forecasts for every point in the history).

Otherwise, you could get the yhat forecast using uncertainty_samples=0 and then compute uncertainty intervals by making repeated calls to https://github.com/facebook/prophet/blob/46e56119835f851714d22b285d2e4081853b9fb1/python/fbprophet/forecaster.py#L1357-L1369 to get samples of yhat (the quantiles of which provide uncertainty intervals), and these calls could be parallelized.