I just posted a simple implementation of WTTE-RNNs in Keras on GitHub: Keras Weibull Time-to-event Recurrent Neural Networks. I’ll let you read up on the details in the linked information, but suffice it to say that this is a specific type of neural net that handles time-to-event prediction in a super intuitive way. If you’re thinking of building a model to predict (rather than understand) churn, I’d definitely consider giving this a shot. And with Keras, implementing the model is pretty darn easy.
As proof of the model’s effectiveness, here’s my demo model (with absolutely no optimization) predicting the remaining useful life of jet engines. It’s not perfect by any means, but it’s definitely giving a pass to engines that are in the clear, and flagging ones that are more likely to fail (plus a few false positives). I have no idea how much better it could do with some tweaking:
Also, if anybody’s curious as to why I’ve been in a bit of a post desert lately, my wife and I recently had a baby and I haven’t been giving as much thought to the blog. However, I have some ideas brewing!