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
accept all cookies2. 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.
For those interested in transformers with time series, I recommend reading this paper: https://arxiv.org/pdf/2205.13504. There is also plenty of other research showing that transformers-based time series models generally underperform much simpler alternatives like boosted trees.
ziofill•4h ago
pealco•3h ago
almosthere•2h ago
bestest•2h ago
document.body.onwheel = (e) => e.stopPropagation();
rossant•2h ago