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Has anyone else struggled with Prophet? I've experimented with it on a few real world datasets and I've had very inconsistent results.
I'm no time series expert, but from my experience and what I've heard, using Prophet for time series forecasting isn't recommended. It often leads to less-than-ideal results. Curiously, in Medium-like (ie low effort) publications it's still the recommended way to tackle a forecasting problem. The promise of a model that can solve any time series problem sounds great, but not all that glitters is gold, and as you get…
Isn't recommended by whom?
Can someone explain why the "no free lunch theorem" does not cause problems here? https://en.wikipedia.org/wiki/No_free_lunch_theorem
First: Prophet is not actually "one model", it's closer to a non-parametric approach than just a single model type. This adds a lot of flexibility on the class of problems it can handle. With that said, Prophet is "flexible" not "universal". A time series of entirely random integers selected from range(0,10) will be handled quite poorly, but fortunately nobody cares about modeling this case.
Second: the same reason that only a small handful of possible stats/ML models get used on virtually all problems. Most problems which people solve with stats/ML share a number of common features which makes it appropriate to use the same model on them (the model's "assumptions"). Applications which don't have these features get treated as edge-cases and ignored, or you write a paper introducing a new type of model to handle it. Consider any ARIMA-type time series model. These are used all the time for many different problem spaces, and are going to do reasonably well on "most" "common" stochastic processes you encounter in "nature", because its constructed to resemble many types of natural processes. It's possible (trivial, even) to conceive of a stochastic process which ARIMA can't really handle (any non-stationary process will work), but in practice most things that ARIMA utterly fails for are not very interesting to model or we have models that work better for that case.
I'm no time series expert, but from my experience and what I've heard, using Prophet for time series forecasting isn't recommended. It often leads to less-than-ideal results. Curiously, in Medium-like (ie low effort) publications it's still the recommended way to tackle a forecasting problem. The promise of a model that can solve any time series problem sounds great, but not all that glitters is gold, and as you get…
[1] - https://ryxcommar.com/2021/11/06/zillow-prophet-time-series-...
I'm no time series expert, but from my experience and what I've heard, using Prophet for time series forecasting isn't recommended. It often leads to less-than-ideal results. Curiously, in Medium-like (ie low effort) publications it's still the recommended way to tackle a forecasting problem. The promise of a model that can solve any time series problem sounds great, but not all that glitters is gold, and as you get…
I'd be curious to see how it performs on economics data compared to mainstream models (say DSGE) whose results have never impressed me with their predictive power.
Has anyone else struggled with Prophet? I've experimented with it on a few real world datasets and I've had very inconsistent results.
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 som…
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
[disclaimer I'm a maintainer of Hamilton] Otherwise FYI Prophet gels well with https://github.com/DAGWorks-Inc/hamilton for setting up your features and dataset for fitting & prediction[/disclaimer].
If anyone is not aware there are many periodic phenomena in astronomy - e.g. variable stars which can have periods from minutes to hundreds of days.
The description of this library sounds like it's very tied to the human world - talking about yearly, weekly and daily seasonality.
[Weirdly though, we do sometimes see variability on 'human' timescales in astronomical data series. If maintenance is carried out weekly on a Monday that can add a signal into the data through missing datapoints.]
Has anyone else struggled with Prophet? I've experimented with it on a few real world datasets and I've had very inconsistent results.
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 som…
> 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).