Guaranteed there are hedge funds with language models that can predict time series. Alot of really good time series research has never been published, and is locked in some guys head that lives in a 20 million dollar apartment in NYC
Can confirm, kdb+ exists… and you’ll probably never be able to get your hands on it. There are lots of models that use it. And they are indeed locked inside some guys head high up in the towers of midtown.
One of the difficulties with these models would be backtesting investment strategies. You always need to make sure that you are only using data that would have been available at the time to avoid look-ahead bias.
"Stanford Repo Released Sep 31, 2025" Seems like something sampled from a distribution with non-zero probability that the day after Sep 30, 2025 would is the 31st....
The underlying work is something called "Flamingo".[1] This is a system for understanding interleaved text and images in sequence. So it can process two "modalities" that are both sequential. This new work seems to put some kind of time token in one "modality" channel, leading to more awareness of time.
(The web site is too cute. Applying a left to right gradient on text is a bit much.)
Foundation models excel at text, images, audio, and video, but lack temporal reasoning capabilities over time-series data streams that run the real world: vitals, prices, telemetry, grid loads, clickstreams, machine logs, business processes.
Time Series Language Models (TSLMs) are open foundation models, supporting time‑series as a native modality, next to text, letting users ask questions, get explanations, and recommendations, all in natural language.
The OpenTSLM White Paper released today demonstrates state-of-the-art temporal reasoning performance. Unlike prior approaches, the cross-attention architecture scales to long time-series remaining viable at scale.
The results:
- Sleep staging: 4.4× accuracy with a model 200× smaller (~880× efficiency)
- Activity recognition: ~6× accuracy with 200× smaller (~1,000× efficiency)
- ECG interpretation: ~2× accuracy with 200× smaller (~400× efficiency)
— first model to process 12-lead ECG signals and text simultaneously with chain-of-thought reasoning validated by cardiologists.
For the first time, foundation models can handle multiple time-series streams of varying lengths concurrently, integrate them with textual context, and produce interpretable explanations (verified by domain experts, clinicians).
This work is the result of a growing collaboration between researchers from Stanford, ETH Zurich, UIUC, University of St. Gallen, University of Washington, Google, and Amazon.
It points to the next foundation model frontier: temporal intelligence that unlocks proactive healthcare, adaptive robotics, resilient infrastructure, and new forms of human-AI collaboration.
This is very cool! From the paper, this technique seems to work well for question answering in time-series.
In medical AI, IMO, the most exciting work is detecting disease signals too subtle for humans—for example, estimating ejection fraction from an ECG (which cardiologists can’t do this, but algorithms can and have been tested in RCTs: https://www.nature.com/articles/s41591-021-01335-4
).
Since OpenTSLM tokenizes time-series into an LLM embedding space, would that process prevent capturing such subtle signals? Or could the approach be extended to handle that use case?
OpenTSLM models are exactly made to capture these subtle signals.
That was one of the original motivations. The model integrates the raw time series data via cross attention, with concrete time series representations learned by a raw time series encoder.
Can you explain how? If I'm understanding the paper right, the timeseries encoding is a Conv1D and the cross-attention layer is constrained to output the token space of a pre-trained LLM. My naive expectation is these constraints would make the model less expressive / fine-tunable to pick up on these types of subtle signals.
But obviously ML is an empirical field, so if you found that a constrained architecture worked well in practice, that's an interesting result in its own right.
Sure! There is more after the 1D conv, another transformer architecture that encodes further features of the time series. The LLM can then basically query this encoder for information, also able to capture more subtle patterns. In away it's similiar to how some vision language models work.
Looks promising! I'll try it once I get home today.
I work with a large number of audio time series data (not words and all have subtle variation). It would be interesting to see how it compares to traditional statistical methods.
Wouldn't it be better to have the model write a script that calls a TS library and give it access to an interpreter to run it? That's how a human would do it. I'm not convinced of the need to bake this into the model. What can you do with native TS capability that you can't by tool calling?
Anthropic is encouraging the "have the model write a script" technique as well, buried in their latest announcement on Claude Agent SDK, this stuck with me:
> The Claude Agent SDK excels at code generation—and for good reason. Code is precise, composable, and infinitely reusable, making it an ideal output for agents that need to perform complex operations reliably.
> When building agents, consider: which tasks would benefit from being expressed as code? Often, the answer unlocks significant capabilities.
There are papers on that, such as https://arxiv.org/abs/2410.15319. Time series modeling will not bring about an understanding of causality except in a weak sense https://en.wikipedia.org/wiki/Granger_causality. To truly connect a cause and effect you need a graphical model. And automated causal discovery, the hardest part of which is proposing the nodes of the graph, is a nascent field.
I think you missed the point. Would you call an image analysis library to describe an image or reason over a sequence of images? Check out some of the plots in the paper to see what these models can do.
I would if the image analysis library was backed by a VLM. I have not fully read the paper, but couldn't figure 6 have been done by an LLM writing a script that calls libraries for time series feature extraction and writing a hypothesis test or whatever? They will do the heavy lifting and return a likelihood ratio or some statistic that is interpretable to an LLM.
> A universal TSLM will power proactive healthcare, adaptive robotics, resilient infrastructure, and new forms of human-AI collaboration.
> scientists, engineers, and builders from ETH, Stanford, Harvard, Cambridge, TUM, CDTM, Google, Meta, AWS, and beyond
What's with all this fuss? Why not just upload your paper to arxiv? Time series models are interesting enough, but from the abstract it's not even clear whether they are using transformers or a recurrent architecture like xLSTM - arguably a more intuitive choice for time series - or something else. This website is barely distinguishable from a crypto/DeFi pitch.
The full paper is on the website. The arxive release of the exact same paper is pending. Click the button "read the white paper" to get the full paper.
Guaranteed there are hedge funds with language models that can predict time series. Alot of really good time series research has never been published, and is locked in some guys head that lives in a 20 million dollar apartment in NYC
Can confirm, kdb+ exists… and you’ll probably never be able to get your hands on it. There are lots of models that use it. And they are indeed locked inside some guys head high up in the towers of midtown.
One of the difficulties with these models would be backtesting investment strategies. You always need to make sure that you are only using data that would have been available at the time to avoid look-ahead bias.
I doubt those are language models.
Why would they use LLM for this?
"Stanford Repo Released Sep 31, 2025" Seems like something sampled from a distribution with non-zero probability that the day after Sep 30, 2025 would is the 31st....
Thanks for the note. Ironically, the post is about models built to understand time.
The underlying work is something called "Flamingo".[1] This is a system for understanding interleaved text and images in sequence. So it can process two "modalities" that are both sequential. This new work seems to put some kind of time token in one "modality" channel, leading to more awareness of time.
(The web site is too cute. Applying a left to right gradient on text is a bit much.)
[1] https://arxiv.org/pdf/2204.14198
Foundation models excel at text, images, audio, and video, but lack temporal reasoning capabilities over time-series data streams that run the real world: vitals, prices, telemetry, grid loads, clickstreams, machine logs, business processes.
Time Series Language Models (TSLMs) are open foundation models, supporting time‑series as a native modality, next to text, letting users ask questions, get explanations, and recommendations, all in natural language.
The OpenTSLM White Paper released today demonstrates state-of-the-art temporal reasoning performance. Unlike prior approaches, the cross-attention architecture scales to long time-series remaining viable at scale.
The results:
- Sleep staging: 4.4× accuracy with a model 200× smaller (~880× efficiency)
- Activity recognition: ~6× accuracy with 200× smaller (~1,000× efficiency)
- ECG interpretation: ~2× accuracy with 200× smaller (~400× efficiency)
— first model to process 12-lead ECG signals and text simultaneously with chain-of-thought reasoning validated by cardiologists.
For the first time, foundation models can handle multiple time-series streams of varying lengths concurrently, integrate them with textual context, and produce interpretable explanations (verified by domain experts, clinicians).
This work is the result of a growing collaboration between researchers from Stanford, ETH Zurich, UIUC, University of St. Gallen, University of Washington, Google, and Amazon.
It points to the next foundation model frontier: temporal intelligence that unlocks proactive healthcare, adaptive robotics, resilient infrastructure, and new forms of human-AI collaboration.
OpenTSLM:https://www.opentslm.com/ Stanford Repo: https://github.com/StanfordBDHG/OpenTSLM
This is very cool! From the paper, this technique seems to work well for question answering in time-series.
In medical AI, IMO, the most exciting work is detecting disease signals too subtle for humans—for example, estimating ejection fraction from an ECG (which cardiologists can’t do this, but algorithms can and have been tested in RCTs: https://www.nature.com/articles/s41591-021-01335-4 ).
Since OpenTSLM tokenizes time-series into an LLM embedding space, would that process prevent capturing such subtle signals? Or could the approach be extended to handle that use case?
OpenTSLM models are exactly made to capture these subtle signals. That was one of the original motivations. The model integrates the raw time series data via cross attention, with concrete time series representations learned by a raw time series encoder.
Can you explain how? If I'm understanding the paper right, the timeseries encoding is a Conv1D and the cross-attention layer is constrained to output the token space of a pre-trained LLM. My naive expectation is these constraints would make the model less expressive / fine-tunable to pick up on these types of subtle signals.
But obviously ML is an empirical field, so if you found that a constrained architecture worked well in practice, that's an interesting result in its own right.
Sure! There is more after the 1D conv, another transformer architecture that encodes further features of the time series. The LLM can then basically query this encoder for information, also able to capture more subtle patterns. In away it's similiar to how some vision language models work.
Looks promising! I'll try it once I get home today.
I work with a large number of audio time series data (not words and all have subtle variation). It would be interesting to see how it compares to traditional statistical methods.
This is very cool! Amazing work guys!
Wouldn't it be better to have the model write a script that calls a TS library and give it access to an interpreter to run it? That's how a human would do it. I'm not convinced of the need to bake this into the model. What can you do with native TS capability that you can't by tool calling?
Anthropic is encouraging the "have the model write a script" technique as well, buried in their latest announcement on Claude Agent SDK, this stuck with me:
> The Claude Agent SDK excels at code generation—and for good reason. Code is precise, composable, and infinitely reusable, making it an ideal output for agents that need to perform complex operations reliably.
> When building agents, consider: which tasks would benefit from being expressed as code? Often, the answer unlocks significant capabilities.
https://www.anthropic.com/engineering/building-agents-with-t...
Does it actually have a concept of time? Does it understand causality?
There are papers on that, such as https://arxiv.org/abs/2410.15319. Time series modeling will not bring about an understanding of causality except in a weak sense https://en.wikipedia.org/wiki/Granger_causality. To truly connect a cause and effect you need a graphical model. And automated causal discovery, the hardest part of which is proposing the nodes of the graph, is a nascent field.
I think you missed the point. Would you call an image analysis library to describe an image or reason over a sequence of images? Check out some of the plots in the paper to see what these models can do.
I would if the image analysis library was backed by a VLM. I have not fully read the paper, but couldn't figure 6 have been done by an LLM writing a script that calls libraries for time series feature extraction and writing a hypothesis test or whatever? They will do the heavy lifting and return a likelihood ratio or some statistic that is interpretable to an LLM.
If you view a byte sequence as a time series then I suppose this could be a good file compression algorithm.
Like hitting a thumb tack with a sledge hammer
> Read the White Paper
> A universal TSLM will power proactive healthcare, adaptive robotics, resilient infrastructure, and new forms of human-AI collaboration.
> scientists, engineers, and builders from ETH, Stanford, Harvard, Cambridge, TUM, CDTM, Google, Meta, AWS, and beyond
What's with all this fuss? Why not just upload your paper to arxiv? Time series models are interesting enough, but from the abstract it's not even clear whether they are using transformers or a recurrent architecture like xLSTM - arguably a more intuitive choice for time series - or something else. This website is barely distinguishable from a crypto/DeFi pitch.
The full paper is on the website. The arxive release of the exact same paper is pending. Click the button "read the white paper" to get the full paper.
[flagged]
Please don't treat people in a hostile fashion when discussing their work on HN. That's the opposite of the kind of community we want here.
https://news.ycombinator.com/newsguidelines.html
OF COURSE the good stuff is proprietary....
Very cool!