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Prophet: Automatic Forecasting Procedure

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Re: Prophet: Automatic Forecasting Procedure

#41

Post by mochomocha »

nighthawk454 wrote: post #27

Model development on Prophet stopped this year: https://medium.com/@cuongduong_35162/facebook-prophet-in-202... They recommend checking out these for cutting-edge time series forecasting: https://neuralprophet.com/ https://nixtla.github.io/statsforecast/

Fun fact: if you don't care about the auto-regressive aspect of NeuralProphet (it's turned off by default), you can implement the core of NeuralProphet/Prophet (piecewise linear trend + Fourier on weekly/daily seasonality) in about 60 LOCs with no other dependency than either torch or numpy+scipy.optimize, and without having to deal with Stan or the very poorly chosen heuristics of neuralprophet.

Another thing that both NeuralProphet and Prophet do extremely wrong by default is uncertainty estimation. The coverage probabilities are way off.

Re: Prophet: Automatic Forecasting Procedure

#42

Post by beckingz »

simonhughes22 wrote: post #38

Wondering how many people are now downloading this and other libs like Dart and trying to do stock market prediction or crypto price forecasting. Most of the devs i know, myself included, have dabbled in coding up trading algorithms at some point in time.

It's the classic data nerd trap.

"I'm pretty good at statistics and can predict things using software... I bet I could make money in the stock market"

And then they realize just how hard it is.

Re: Prophet: Automatic Forecasting Procedure

#43

Post by Donald »

mochomocha wrote: post #41
nighthawk454 wrote: post #27

Model development on Prophet stopped this year: https://medium.com/@cuongduong_35162/facebook-prophet-in-202... They recommend checking out these for cutting-edge time series forecasting: https://neuralprophet.com/ https://nixtla.github.io/statsforecast/

Fun fact: if you don't care about the auto-regressive aspect of NeuralProphet (it's turned off by default), you can implement the core of NeuralProphet/Prophet (piecewise linear trend + Fourier on weekly/daily seasonality) in about 60 LOCs with no other dependency than either torch or numpy+scipy.optimize, and without having to deal with Stan or the very poorly chosen heuristics of neuralprophet. Another thing that b…

Do you have an example implementation of reimplementing the core of these?

Re: Prophet: Automatic Forecasting Procedure

#44

Post by 3abiton »

mochomocha wrote: post #41
nighthawk454 wrote: post #27

Model development on Prophet stopped this year: https://medium.com/@cuongduong_35162/facebook-prophet-in-202... They recommend checking out these for cutting-edge time series forecasting: https://neuralprophet.com/ https://nixtla.github.io/statsforecast/

Fun fact: if you don't care about the auto-regressive aspect of NeuralProphet (it's turned off by default), you can implement the core of NeuralProphet/Prophet (piecewise linear trend + Fourier on weekly/daily seasonality) in about 60 LOCs with no other dependency than either torch or numpy+scipy.optimize, and without having to deal with Stan or the very poorly chosen heuristics of neuralprophet. Another thing that b…

Why is STAN viewed negatively in this light? I am curious why bayesian libraries are the black sheep.

Re: Prophet: Automatic Forecasting Procedure

#45

Post by chubs »

On this topic, does anyone know of a suitable time-series forecaster for multivariate analysis? Eg 8 independent/input variables, and one output variable? I've been using multiple linear regression (which works impressively!) but it doesn't take into account the time series, only the single prior day of inputs. Thanks :)

Re: Prophet: Automatic Forecasting Procedure

#46

Post by SpaceManNabs »

beckingz wrote: post #42
simonhughes22 wrote: post #38

Wondering how many people are now downloading this and other libs like Dart and trying to do stock market prediction or crypto price forecasting. Most of the devs i know, myself included, have dabbled in coding up trading algorithms at some point in time.

It's the classic data nerd trap. "I'm pretty good at statistics and can predict things using software... I bet I could make money in the stock market" And then they realize just how hard it is.

the hard part isn't the stats. it is all the information that people buy and setting up those ingest pipelines! If i had a satellite telling me when a certain big company has a lot of cars in the lot parked after hours, I could make a zillion bucks too!

Re: Prophet: Automatic Forecasting Procedure

#47

Post by ImaCake »

rdli wrote: post #30

As others have pointed out, Prophet is not a particularly good model for forecasting, and has been superseded by a multitude of other models. If you want to do time series forecasting, I'd recommend using Darts: https://github.com/unit8co/darts . Darts implements a wide range of models and is fairly easy to use. The problem with time series forecasting in general is that they make a lot of assumptions on the shape of…

> they expect that your data comes at a very regular interval

Does prophet rely on this assumption? For health timeseries data the tool of choice is survival analysis - typically using Cox proportional hazards regression or similar regression tools that are able to handle irregular or censored data.

I've seen some moves towards using fancy bayesian or fancier machine learning stuff for clinical trials but a big issue is that they are very difficult to communicate to their intended audience.

Re: Prophet: Automatic Forecasting Procedure

#48

Post by naijaboiler »

chubs wrote: post #45

On this topic, does anyone know of a suitable time-series forecaster for multivariate analysis? Eg 8 independent/input variables, and one output variable? I've been using multiple linear regression (which works impressively!) but it doesn't take into account the time series, only the single prior day of inputs. Thanks :)

i use GLM with my own hand-crafted features based on my knowledge of the business and the things that influence it. Works very very well

Re: Prophet: Automatic Forecasting Procedure

#49

Post by nextos »

3abiton wrote: post #44
mochomocha wrote: post #41

Earlier quoted context omitted.

Fun fact: if you don't care about the auto-regressive aspect of NeuralProphet (it's turned off by default), you can implement the core of NeuralProphet/Prophet (piecewise linear trend + Fourier on weekly/daily seasonality) in about 60 LOCs with no other dependency than either torch or numpy+scipy.optimize, and without having to deal with Stan or the very poorly chosen heuristics of neuralprophet. Another thing that b…

Why is STAN viewed negatively in this light? I am curious why bayesian libraries are the black sheep.

I am curious too. I have used Stan extensively. I found it extremely polished and pleasant to use.

It generated very efficient samplers for particularly weird (and enormous!) hierarchical models I had. Documentation is also great.

It is also worth reading Andrew Gelman's post about Prophet: https://statmodeling.stat.columbia.edu/2017/03/01/facebooks-...

Re: Prophet: Automatic Forecasting Procedure

#50

Post by globamel »

nighthawk454 wrote: post #27

Model development on Prophet stopped this year: https://medium.com/@cuongduong_35162/facebook-prophet-in-202... They recommend checking out these for cutting-edge time series forecasting: https://neuralprophet.com/ https://nixtla.github.io/statsforecast/

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