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.