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 anything useful for "business applications" of time series like sales forecasting.
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.
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.
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 flexibly incorporate a wide range of useful signals, such as:
* Weather forecasts for a region
* Sales from similar products or related categories
* Data from nearby locations or stations
* More fine-granular recent interactions/activities
* Price changes and promotional campaigns
* Competitor data (e.g., pricing, availability)
* Aggregated regional or market-level statistics
The architecture is modular: we don't default to a Transformer for the past sequence component (and in fact use a simpler architecture). The Graph Transformer/Graph Neural Network then extends the past sequence component by aggregating from additional sources.
> 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.
That’s a bold claim. All of our academic work is conducted in collaboration with university partners, is peer-reviewed, and has been accepted at top-tier conferences. Sharing blog posts that explain the design decisions behind our models isn’t a conflict of interest—it's part of making our internals more transparent.
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.
Would you be so kind as to recommend some resources on modern, promising methods for time series forecasting? I'm starting a position doing this work soon and would like to learn more about it if you'd be willing to share
Read all the M series of competitions and the papers that come out of those exercises. Read Keogh. Also have a healthy respect and understanding of the traditional methods rather than getting distracted by all that happens to be shiny now.
Recent work like Informer (AAAI'21) and Autoformer (NeurIPS'21) have shown competitive performance against statistical methods by addressing the quadratic complexity and long-range dependency issues that plagued earlier transformer architectures for time series tasks.
> 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.
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.
Readit News
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.
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.
> 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 flexibly incorporate a wide range of useful signals, such as:
* Weather forecasts for a region
* Sales from similar products or related categories
* Data from nearby locations or stations
* More fine-granular recent interactions/activities
* Price changes and promotional campaigns
* Competitor data (e.g., pricing, availability)
* Aggregated regional or market-level statistics
The architecture is modular: we don't default to a Transformer for the past sequence component (and in fact use a simpler architecture). The Graph Transformer/Graph Neural Network then extends the past sequence component by aggregating from additional sources.
> 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.
That’s a bold claim. All of our academic work is conducted in collaboration with university partners, is peer-reviewed, and has been accepted at top-tier conferences. Sharing blog posts that explain the design decisions behind our models isn’t a conflict of interest—it's part of making our internals more transparent.
> 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?
document.body.onwheel = (e) => e.stopPropagation();
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