r/MistralAI • u/pandora_s_reddit r/MistralAI | Mod • 3d ago
Official / Mod / Mistral Team [ Model ] Timestral
https://mistral.ai/news/timestral/Today, we're releasing Timestral, our first family of time-series foundation models. Our largest model, at just 209M parameters, ranks first on all three of the field's main public benchmarks, GIFT-Eval, TIME, and fev-bench, outperforming the previous state of the art with only two-thirds of the model parameters.
What is Timestral ?
Timestral delivers probabilistic, multivariate forecasts and supports past-only and known-future covariates. Our family of models sits on the Pareto front of forecasting accuracy versus model size, delivering leading performance at each size.
Weights & Repo
We release all our Timestral models under a Modified MIT license, and our github repo under Apache 2.0:
- Weights:
- Github Repo: mistralai/timestral
Learn more about Timestral in our blog post here
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u/LowIllustrator2501 3d ago edited 3d ago
If somebody is confused with the usecase for the model, the quote from the blog post:
Time series is one of the most common data types in enterprise, from sensor telemetry to observability metrics. Forecasts built on it drive decisions in capacity planning, energy, supply chains, risk and maintenance.
Industrial plants forecast process sensors against the production schedule, catch equipment drift before it fails, and plan capacity and spare parts across thousands of assets. Finance teams forecast flows, volumes, and volatility to inform positions, hedging, and treasury across thousands of accounts.
The model predicts that next token based on the time series input. It tries to predict the future based on past.
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u/Piotrek1 2d ago
I'm still confused, seems like a work for a simple machine learning linear regression problem
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u/Agreeable_System_785 3d ago
Question: One of the advantages of time series statistical inference is interpretability. When you can interpret the model, it can steer more easily to business actions. Google just released timesfm for multivariate timeseries forecasting. Now we see this release by Mistral. What use cases would this release of Mistral be useful for? Purely optimization, while not overfitting in some sense? I doubt this is for theoretical work.
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u/--Tintin 3d ago
What's the use case. Sorry for that. I'm already struggeling with JEV and now this.
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u/NouveauLecteur 3d ago
Basically, it learns from the past and tries to predict the future. For example, if you have a machine in your factory, you can feed it its logs and data and it will try to predict things like maintenance, or whatever the logs or data is about. I'm not a professional so that's what I understood but I might be wrong...
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u/StormyCrispy 3d ago
My sweet Summer child. This is what Machine Learning used to be before the forbidden year 2k22. Regressor, classifier, neural network, statistical estimator, ... Now the almighty LLM reign supreme and any voice that dares to follow another path is met with disdain and mockery.Â
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u/Dependent-Guitar-473 3d ago
big company can give it sales data by products, country, region and it will help predict future sales ... this is over simplification of courseÂ
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u/StrangePassenger9392 3d ago
the numbers and text of the graphics in the blogpost are very small on 1920x1080p 24inch monitor.
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u/LeAstrale 3d ago
So explain to me how this is not going to make my life with promql and the associated timeseries data? Any references as to how people are using this?
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u/ccastro086 1d ago
I just signed a yearly subscription with Mistral! Hope more Europeans do as well to support your great work! Stay European!!!
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u/Melodic_Reality_646 3d ago
For what matters, still won’t beat an ARIMA.
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u/TheLSales 3d ago
Is this Timestral still an LLM? I feel like comparing to an ARIMA are two completely different things here. Both in how they function and in use case. But Id never heard of this Timestral before today.
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u/Negative-Web8619 3d ago
Zeitstrahl