Prophet : forecasting at scale by Facebook

Prophet is optimized for the business forecast tasks which typically have any of the following characteristics:

• hourly, daily, or weekly observations with at least a few months (preferably a year) of history
• strong multiple “human-scale” seasonalities: day of week and time of year
• important holidays that occur at irregular intervals that are known in advance (e.g. the Super Bowl)
• a reasonable number of missing observations or large outliers
• historical trend changes, for instance due to product launches or logging changes
• trends that are non-linear growth curves, where a trend hits a natural limit or saturates

We have found Prophet’s default settings to produce forecasts that are often accurate as those produced by skilled forecasters, with much less effort. With Prophet, you are not stuck with the results of a completely automatic procedure if the forecast is not satisfactory — an analyst with no training in time series methods can improve or tweak forecasts using a variety of easily-interpretable parameters. We have found that by combining automatic forecasting with analyst-in-the-loop forecasts for special cases, it is possible to cover a wide variety of business use-cases. The following diagram illustrates the forecasting process we have found to work at scale:

https://research.fb.com/prophet-forecasting-at-scale/

as well:
by Google : https://google.github.io/CausalImpact/CausalImpact.html