Time Series Forecasting with Graph Transformers
kumo.ai
kumo.ai
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.
> 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?
> 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.
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