[ttt333]

Yes. I've tried using it for pretty straightforward time series forecasts, and I struggled to make it into something useful in a business context.

I'll disclaim that I'm just a finance dude and not a data scientist or programmer. But the documentation leads me to believe that I am in the target audience. I felt like I could grasp the basic mechanics after reading the paper, but I wish the documentation could help someone like me be more intelligent with the 'tuning' of the model. I could never get accuracy below 15% average error, which is too large for my use case.

Probably user ignorance, but that's my experience.

[SpaceManNabs]

You are the primary audience. Time series forecasting with deep learning is fraught with inconsistency. Someone on r/ML went pretty hard on detailing a survey and the stuff that was SOTA 10 years ago still is. Wish I saved that thread. The dude was well published.

edit: found it https://www.reddit.com/r/MachineLearning/comments/pe1lst/r_i...

Turns out it was about time series anomaly detection, but if you can detect, you can forecast if your model is generative

[isaacfung]

These may be related.

https://www.reddit.com/r/datascience/comments/11vzjhi/statis...

https://www.reddit.com/user/eamonnkeogh/submitted/?sort=top

[SpaceManNabs]

I updated my comment with the thread but it was actually about time series anomaly detection. Turns out it was the same dude in your second link, and your comment includes forecasting in the first link as well. Thank you!

[Pandabob]

When was this? I might go chasing this lead down, but even a fuzzy estimation of when would help. Will come link it here if I find it.

[SpaceManNabs]

I updated my comment!

[genewitch]

aaaaand i just spent 3 hours watching that, trying to remember some parts of calculus, and reading all of the wikipedia articles and "also see" that were grey on white in the video. Then i fell asleep, but i wanted to thank you, as i also thanked the Prof. that made that video (on reddit).

[eep_social]

This looks to me like something they’d be using for internal capacity planning. If so, they’d be asking it questions like, “how much capacity do we build out for the upcoming holiday rush?” I wouldn't be surprised if financial datasets are very noisy compared to service capacity metrics. I didn’t read the paper though, maybe this is addressed and maybe I’m wrong about the use case! But stuff like the below from the docs reads like capacity planning tool to me:

> As an example, let’s look at a time series of the log daily page views for the Wikipedia page for Peyton Manning. We scraped this data using the Wikipediatrend package in R. Peyton Manning provides a nice example because it illustrates some of Prophet’s features, like multiple seasonality, changing growth rates, and the ability to model special days (such as Manning’s playoff and superbowl appearances).

[philjohn]

Also perhaps anomaly detection in a metric.

[hackernewds]

I'm sad to see no one has responded with a solution to your problem. You are absolutely the target audience, and in my experience, Prophet is "as good as it gets" to generalized forecasting.