Time Series Forecasting with Graph Transformers • Hacker News Board

Re: Time Series Forecasting with Graph Transformers

#41

Post by fumeux_fume » Wed, Jun 18, 2025, 2:04 PM UTC

rusty1s wrote: post #31

cye131 wrote: post #23

I'm not a fan of this blog post as it tries to pass off a method that's not accepted as a good or standard time series methodology (graph transformers) as though it were a norm. Transformers perform poorly on time series, and graph deep learning performs poorly for tasks that don't have real behaviorial/physical edges (physical space/molecules/social graphs etc), so it's unclear why combining them would produce anyth…

Hey, one of the authors here—happy to clarify a few things. > Transformers perform poorly on time series. That’s not quite the point of our work. The model isn’t about using Transformers for time series per se. Rather, the focus is on how to enrich forecasting models by combining historical sequence data with external information, which is often naturally structured as a graph. This approach enables the model to flex…

Lol, a bold claim. It's a rational assumption that any business publishing "academic work" is selling you the upside while omitting or downplaying the downside.

Re: Time Series Forecasting with Graph Transformers

#42

Post by orochimaaru » Wed, Jun 18, 2025, 3:26 PM UTC

tough wrote: post #24

cye131 wrote: post #23

I'm not a fan of this blog post as it tries to pass off a method that's not accepted as a good or standard time series methodology (graph transformers) as though it were a norm. Transformers perform poorly on time series, and graph deep learning performs poorly for tasks that don't have real behaviorial/physical edges (physical space/molecules/social graphs etc), so it's unclear why combining them would produce anyth…

thoughts on TimesFM? > After looking further it seems like this startup is both trying to publish academic research promoting these models as well as selling it to businesses, which seems like a conflict of interest to me. is this a general rule of thumb that one should not use the same organization to publish research and pursue commercialization generally?

Not really. There is no rule against it. You can have a team that research, publishes, patents and shares the patents with commercial scalers. It’s easier with ML than with manufacturing.

Re: Time Series Forecasting with Graph Transformers

#43

Post by Vaslo » Wed, Jun 18, 2025, 9:53 PM UTC

tech_ken wrote: post #20

meindnoch wrote: post #18

[flagged]

> 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 th…

I can’t read the original other than what was quoted, but time series will never make you millions in stocks (or millions more than not using it) because time series uses past information to predict a time series, and much of stock pricing is already set via efficient market that those firms have already beat you to.