81–90 of 114 posts
You talk about having to choose the best algorithm but it seems like Prophet is just another algorithm to choose from. Is there some kind of built in grid-search or are you just stating that results from your AM have been more accurate than ARIMA?
can someone explain what's the meaning of this line > df['y'] = np.log(df['y'])
https://gist.github.com/llimllib/385230f38c3f9b70c3e46158e60...
That being said this claim in point #1 baffles me:
> Prophet makes it much more straightforward to create a reasonable, accurate forecast. The forecast package includes many different forecasting techniques (ARIMA, exponential smoothing, etc), each with their own strengths, weaknesses, and tuning parameters. We have found that choosing the wrong model or parameters can often yield poor results, and it is unlikely that even experienced analysts can choose the correct model and parameters efficiently given this array of choices.
The forecast package contains an auto.arima function which does full parameter optimization using AIC which is just as hands free as is claimed of Prophet. I have been using it commercially and successfully for years now. Maybe prophet produces better models (I'll definitely take a look myself), but to claim that it's not possible to get good results without experience seems a bit disingenuous.
As an aside, anybody interested in a great introductory book on time series forecasting should check out Rob Hyndman's book which is freely available online. https://www.otexts.org/fpp
This looks amazing, congratulations. We're planning to add forecasting to our SaaS analytics product ( https://chartmogul.com ) later this year, I'm going to look and see if we can use this in our product now.
I may do a test implementation into Airbnb Superset actually to see how it flies.
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Stan is nice but its GPL license is taboo in my corporate environment :( . I am puzzled how they managed to release Prophet under BSD with such a dependency.
Stan has a BSD core. Prophet must avoid the GPLv3 interfaces.
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Do you know of any good beginner tutorials for Stan or probabilistic programming in general? All the examples that I found seemed quite complex and I was a bit overwhelmed by all the math. Which might also be a sign that I should brush up my math skills. What kind of math/stats should I revise to be able to better understand probabilistic programming?
Doing Bayesian Data Analysis, Second Edition: A Tutorial with R, JAGS, and Stan. It is very approachable and also has lots of practice problems. It's not a math-heavy book at all. Edit: I wouldn't recommend Probabilistic Programming and Bayesian Methods for Hackers. When I tried using it, I felt that too much was glossed over. The book that I recommend excels at conveying a strong intuition for how these various tech…
I see this being applicable to analysts when deciding on on a company's credit worthiness.
For a corporate credit analyst working at a bank, what are some good introduction material for getting into forecasting using tools like these? I see this being applicable to analysts when deciding on on a company's credit worthiness.
I would think if you're already assigning credit ratings, you can set that as your dependent variable and use things like company revenue, number of employees, age of company, etc. as your independent variables. You can use a number of different models to assess credit worthiness based on this data. Evaluate several to determine the most accurate.
I have been working for a few years on a similar project using evolutionary algorithms on top of other models (linear / ann). It works quite well (e.g., for equidistant energy demand / supply forecasts) but there's still lots of stuff to do. It's major benefit is that it figures out relationship to the target time series by itself, so you can just throw in all time series and see what comes out. Language is Clojure,…
Why not release sooner and document the code smells? Maybe you'll get patches