I once built a forecasting framework for a unicorn startup. Revenue and Pipeline predictability was the key as the company was going through the IPO phase. There were three approaches I took and 'ensemble'd them to predict the revenue and pipeline.
1. Time series based forecast based on revenue (the one OP is referring to). All the statistical time-series models come here. I primarily used H2O.ai for this.
2. Conversion based revenue forecast (input -> pipeline, output -> revenue). This proved to be quite tricky as there was a time lag between pipeline creation and revenue conversion
3. Delphi-method: Got the sales/pre-sales folks on-ground to predict a bottom-up number and used that as a forecast.
Finally, I combined them by applying weightages to the above approaches - based on how accurate they were on the test dataset.
IMHO, Like many of them have pointed out - the model/assumptions are more important than the library. The job of a data scientist is to make the prediction as reliable and explainable as possible.
1. Time series based forecast based on revenue (the one OP is referring to). All the statistical time-series models come here. I primarily used H2O.ai for this.
2. Conversion based revenue forecast (input -> pipeline, output -> revenue). This proved to be quite tricky as there was a time lag between pipeline creation and revenue conversion
3. Delphi-method: Got the sales/pre-sales folks on-ground to predict a bottom-up number and used that as a forecast.
Finally, I combined them by applying weightages to the above approaches - based on how accurate they were on the test dataset.
IMHO, Like many of them have pointed out - the model/assumptions are more important than the library. The job of a data scientist is to make the prediction as reliable and explainable as possible.