I disregard anything on time series forecasting from any entity that uses Facebook Prophet as a benchmark.
frakt0x90 · 2h ago
Prophet is great and we use it for multiple models in production at work. Our industry has tons of weird holidays and seasonality and prophet handles that extremely well.
tech_ken · 2h ago
This is sales research, and after "CAGR in a GSheet" FB Prophet is what's going to be most recognizable to the widest base of customers.
FWIW seems like the real value add is this relational DB model: https://kumo.ai/research/relational-deep-learning-rdl/ The time-series stuff is them just elaborating the basic model structure a little more to account for time-dependence
melenaboija · 3h ago
For such strong and personal statement I have to ask why.
Worksheet · 1h ago
If you arrived into, say, London and googled "Best fish and chips" would you believe that the top result gives you the meal that you're after?
esafak · 3h ago
Why? That is what everybody uses. What do you use?
loehnsberg · 1h ago
L1-regularized autoregressive features, holiday dummies, Fourier terms (if suitable in combination) yield lower test errors, are faster in training, and easier to cross-validate than Prophet.
esafak · 43m ago
With which library though?
ziofill · 3h ago
I can't stand websites that override scrolling
pealco · 3h ago
Most of my time interacting with this site was spent in developer tools, trying to figure out where the scrolling behavior was coming from. (Couldn't figure it out.) I can't understand why people are still doing this in 2025.
Most likely the developer is using a Windows computer.
rossant · 2h ago
I came here to say this. Don't mess with my scrollbar. Ever.
cwmoore · 3h ago
“Here, sign this.”
accept all cookies
meindnoch · 52m ago
1. Stop messing with my scrolling.
2. If this really worked, you'd be making billions on the stock market. The fact that you don't, tells me it doesn't work.
tech_ken · 35m ago
> If this really worked, you'd be making billions on the stock market
That's kind of a weird thing to say given that the market cap for quantitative finance is well over a billion dollars, and this product clearly seems to be targeting that sector (plus others) as a B2B service provider. Do you think that all those quantitative trading firms are using something other than time-series analytics?
Also, setting aside the issue of whether time-series forecasting is valuable for stock-market trading, it seems like the value add of this product isn't necessarily the improved accuracy of the forecasts, but rather the streamlined ETL -> Feature Engineering -> Model Design process. For most firms (either in quantitative finance or elsewhere) that's the work of a small dedicated team of highly-trained specialists. This seems like it has the potential to greatly reduce the labor requirements for such an organization without a concomitant loss of product quality.
FWIW seems like the real value add is this relational DB model: https://kumo.ai/research/relational-deep-learning-rdl/ The time-series stuff is them just elaborating the basic model structure a little more to account for time-dependence
document.body.onwheel = (e) => e.stopPropagation();
2. If this really worked, you'd be making billions on the stock market. The fact that you don't, tells me it doesn't work.
That's kind of a weird thing to say given that the market cap for quantitative finance is well over a billion dollars, and this product clearly seems to be targeting that sector (plus others) as a B2B service provider. Do you think that all those quantitative trading firms are using something other than time-series analytics?
Also, setting aside the issue of whether time-series forecasting is valuable for stock-market trading, it seems like the value add of this product isn't necessarily the improved accuracy of the forecasts, but rather the streamlined ETL -> Feature Engineering -> Model Design process. For most firms (either in quantitative finance or elsewhere) that's the work of a small dedicated team of highly-trained specialists. This seems like it has the potential to greatly reduce the labor requirements for such an organization without a concomitant loss of product quality.