# Mistral announces Timestral, a compact family of time-series forecasting models [Canonical reading](https://escapemeridian.com/readings/2fd3303e1caf51d1822a10544c598b37cc1223829b5fed32571a8aa983ddd528) Mistral says Timestral is its first family of time-series foundation models: models trained to forecast patterns in sequences of data, such as sensor readings. The four models range from 5 million to 209 million parameters and produce probabilistic forecasts across related data series. Mistral reports that its largest model ranked first on three public benchmarks, and says it can run forecasts on a standard server CPU. The company says most training data was synthetic and that a technical report with full results will follow. Headline and summary by Escape Meridian, prepared by Luna from the attributed source. A source check is not independent confirmation of the source’s claims. ## Original source Mistral AI · Article Original source date: Oct 15, 2026 (2026-10-15T12:00:00.000Z). Date basis: Date supplied by the publisher’s feed. Added to Escape Meridian: 2026-10-16T02:29:40.106Z. First observed: 2026-10-15T15:06:37.276Z. Retrieved: 2026-10-16T02:29:03.772Z. - [Timestral: Mistral's time-series foundation models | Mistral](https://mistral.ai/news/timestral/) ## Limits Benchmark rankings and performance figures are reported by Mistral in the supplied publisher page; the evidence does not independently verify them. Mistral says results were submitted to the GIFT-Eval and TIME leaderboards and that a technical report with full results will follow. ## Open question How well will Timestral’s reported benchmark performance transfer to real-world forecasting tasks? ## Source-stated Mistral describes Timestral as its first family of time-series foundation models. > Today, we’re releasing Timestral, our first family of time-series foundation models. - [Supporting source](https://mistral.ai/news/timestral/) ## Source-stated Mistral says the four-model family ranges from 5 million to 209 million parameters and supports probabilistic forecasts across multiple related series, with past and known-future covariates as inputs. > Timestral delivers probabilistic, multivariate forecasts and supports past-only and known-future covariates. - [Supporting source](https://mistral.ai/news/timestral/) ## Source-stated Mistral reports that its 209-million-parameter model ranked first on three public forecasting benchmarks; it says the results were submitted to the GIFT-Eval and TIME leaderboards. > 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. - [Supporting source](https://mistral.ai/news/timestral/) ## Source-stated Mistral says Timestral can run inference on a standard server CPU and gives example runtimes for forecasts one day ahead from three weeks of 15-minute history. > On a standard server CPU with 8 threads, forecasting one day ahead from three weeks of 15-minute history takes 10 ms with the 5M model and 140 ms with the 209M model. - [Supporting source](https://mistral.ai/news/timestral/) ## Source-stated Mistral says most of Timestral’s training data was synthetic, followed by training on a mix of real and synthetic data. > The majority of the training data was synthetic. - [Supporting source](https://mistral.ai/news/timestral/) ## Source and contributor context Mistral AI · Publisher The publisher is identified here. No individual author has been inferred. ## Source media Source-associated media was found; no additional asset was selected for this reading. The original source remains available. ## Terms and references Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement. ## Time-series Observations arranged over time, such as monthly sales; forecasting uses their patterns to estimate later values. - [Definition reference](https://developers.google.com/machine-learning/glossary#time-series-analysis) ## Benchmark A defined set of tests used to compare systems. Results depend on the tasks and measures selected. - [Definition reference](https://arxiv.org/abs/2211.09110) ## Mistral AI Mistral AI develops models and products with several options for how organizations deploy them. - [Explore this context](/entities/mistral-ai) - [Reference](https://mistral.ai/news/) ## Editorial provenance Version 1. Publication ID: e2a78c3d-0930-49da-80ad-1326c317909a. Prepared by gpt-6-luna; grounding review by gpt-6-luna. Source check: passed by anthropic/claude-opus-5.5 on 2026-10-16T02:29:40.106Z. This checks fidelity to the source, not whether its claims are independently true. Editorial selection: accept; reasons: material\_development. Observation: 9e82dc1f1b422b8ae48a53d5b2e7eb8c74297de3a739acd5bdb3046c9f863243. Source text hash: a1835f90ccbe422f35f2db106fe49906ce32a70c1873635c85b261a31aa38140. ## Revision and source-check history ### Version 1 — 2026-10-16T02:29:40.106Z Mistral says Timestral is its first family of time-series foundation models: models trained to forecast patterns in sequences of data, such as sensor readings. The four models range from 5 million to 209 million parameters and produce probabilistic forecasts across related data series. Mistral reports that its largest model ranked first on three public benchmarks, and says it can run forecasts on a standard server CPU. The company says most training data was synthetic and that a technical report with full results will follow. ## Original source Mistral AI · Article Original source date: Oct 15, 2026 (2026-10-15T12:00:00.000Z). Date basis: Date supplied by the publisher’s feed. Added to Escape Meridian: 2026-10-16T02:29:40.106Z. First observed: 2026-10-15T15:06:37.276Z. Retrieved: 2026-10-16T02:29:03.772Z. - [Timestral: Mistral's time-series foundation models | Mistral](https://mistral.ai/news/timestral/) ## Limits Benchmark rankings and performance figures are reported by Mistral in the supplied publisher page; the evidence does not independently verify them. Mistral says results were submitted to the GIFT-Eval and TIME leaderboards and that a technical report with full results will follow. ## Open question How well will Timestral’s reported benchmark performance transfer to real-world forecasting tasks? ## Source-stated Mistral describes Timestral as its first family of time-series foundation models. > Today, we’re releasing Timestral, our first family of time-series foundation models. - [Supporting source](https://mistral.ai/news/timestral/) ## Source-stated Mistral says the four-model family ranges from 5 million to 209 million parameters and supports probabilistic forecasts across multiple related series, with past and known-future covariates as inputs. > Timestral delivers probabilistic, multivariate forecasts and supports past-only and known-future covariates. - [Supporting source](https://mistral.ai/news/timestral/) ## Source-stated Mistral reports that its 209-million-parameter model ranked first on three public forecasting benchmarks; it says the results were submitted to the GIFT-Eval and TIME leaderboards. > 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. - [Supporting source](https://mistral.ai/news/timestral/) ## Source-stated Mistral says Timestral can run inference on a standard server CPU and gives example runtimes for forecasts one day ahead from three weeks of 15-minute history. > On a standard server CPU with 8 threads, forecasting one day ahead from three weeks of 15-minute history takes 10 ms with the 5M model and 140 ms with the 209M model. - [Supporting source](https://mistral.ai/news/timestral/) ## Source-stated Mistral says most of Timestral’s training data was synthetic, followed by training on a mix of real and synthetic data. > The majority of the training data was synthetic. - [Supporting source](https://mistral.ai/news/timestral/) ## Source and contributor context Mistral AI · Publisher The publisher is identified here. No individual author has been inferred. ## Source media Source-associated media was found; no additional asset was selected for this reading. The original source remains available. ## Terms and references Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement. ## Time-series Observations arranged over time, such as monthly sales; forecasting uses their patterns to estimate later values. - [Definition reference](https://developers.google.com/machine-learning/glossary#time-series-analysis) ## Benchmark A defined set of tests used to compare systems. Results depend on the tasks and measures selected. - [Definition reference](https://arxiv.org/abs/2211.09110) ## Mistral AI Mistral AI develops models and products with several options for how organizations deploy them. - [Explore this context](/entities/mistral-ai) - [Reference](https://mistral.ai/news/) ## Editorial provenance Version 1. Publication ID: e2a78c3d-0930-49da-80ad-1326c317909a. Prepared by gpt-6-luna; grounding review by gpt-6-luna. Source check: passed by anthropic/claude-opus-5.5 on 2026-10-16T02:29:40.106Z. This checks fidelity to the source, not whether its claims are independently true. Editorial selection: accept; reasons: material\_development. Observation: 9e82dc1f1b422b8ae48a53d5b2e7eb8c74297de3a739acd5bdb3046c9f863243. Source text hash: a1835f90ccbe422f35f2db106fe49906ce32a70c1873635c85b261a31aa38140. ### passed — 2026-10-16T02:29:40.106Z — anthropic/claude-opus-5.5 Publication: e2a78c3d-0930-49da-80ad-1326c317909a attribution — pass: The draft consistently attributes the claims to Mistral, matching the publisher page authored by Mistral. > Today, we’re releasing Timestral, our first family of time-series foundation models. summary — pass: The title, summary and question accurately reflect the announcement: four model sizes, probabilistic multivariate forecasts, reported benchmark rankings, CPU inference, mostly synthetic training data and a forthcoming technical report. The question about real-world transfer fits the source's own discussion of adaptation. > Timestral has been optimised as a family of four models: 5M, 28M, 123M and 209M parameters. date — pass: The originalPublished value of 2026-10-15 matches the dateline on the page. > Introducing Timestral October 15, 2026 support — pass: Each claim is directly supported by the source text, including the leaderboard submissions and the two-stage training on synthetic and then mixed data. The draft frames the benchmark results as self-reported. > Timestral is first pre-trained at scale on purely synthetic data with context size 8192, then post-trained on a mix of real and synthetic data with a 4x longer context. media — pass: The selected media is the verified direct link to the original Mistral announcement, which is the most relevant access point to the source. ## Corrections and challenges [Send a correction or an opposing source](https://escapemeridian.com/corrections/) [JSON record](https://escapemeridian.com/readings/2fd3303e1caf51d1822a10544c598b37cc1223829b5fed32571a8aa983ddd528.json)