title: This Time is Different: An Observability Perspective on Time Series
    Foundation Models
summary: We introduce Toto, a time series forecasting foundation model with 151
  million parameters. Toto uses a modern decoder-only architecture coupled with
  architectural innovations designed to account for specific challenges found in
  multivariate observability time series data. Toto's pre-training corpus is a
  mixture of observability data, open datasets, and synthetic data, and is
  4-10times larger than those of leading time series foundation models.
  Additionally, we introduce BOOM, a large-scale benchmark consisting of 350
  million observations across 2,807 real-world time series. For both Toto and
  BOOM, we source observability data exclusively from Datadog's own telemetry and
  internal observability metrics. Extensive evaluations demonstrate that Toto
  achieves state-of-the-art performance on both BOOM and on established general
  purpose time series forecasting benchmarks. Toto's model weights, inference
  code, and evaluation scripts, as well as BOOM's data and evaluation code, are
  all available as open source under the Apache 2.0 License available at
  https://huggingface.co/Datadog/Toto-Open-Base-1.0 and
  https://github.com/DataDog/toto.
paper_authors._id: 682f037527c6b9d4500ded82
paper_authors.name: Ben Cohen
paper_authors._id: 682f037527c6b9d4500ded83
paper_authors.name: Emaad Khwaja
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paper_authors.fullname: Emaad Khwaja
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paper_authors.name: Youssef Doubli
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paper_authors.name: Salahidine Lemaachi
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paper_authors.fullname: Salahidine Lemaachi
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paper_authors.user: Salahidine2002
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paper_authors.name: Chris Lettieri
paper_authors._id: 682f037527c6b9d4500ded87
paper_authors.name: Charles Masson
paper_authors._id: 682f037527c6b9d4500ded88
paper_authors.name: Hugo Miccinilli
paper_authors._id: 682f037527c6b9d4500ded89
paper_authors.name: Elise Ramé
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paper_authors.fullname: Elise Ramé
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paper_authors.user: eliserame
paper_authors._id: 682f037527c6b9d4500ded8a
paper_authors.name: Qiqi Ren
paper_authors._id: 682f037527c6b9d4500ded8b
paper_authors.name: Afshin Rostamizadeh
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paper_authors.fullname: Afshin Rostamizadeh
paper_authors.type: user
paper_authors.user: afshinrostami
paper_authors._id: 682f037527c6b9d4500ded8c
paper_authors.name: Jean Ogier du Terrail
paper_authors._id: 682f037527c6b9d4500ded8d
paper_authors.name: Anna-Monica Toon
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-26T08:18:25.456Z
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paper_authors.fullname: Anna-Monica Toon
paper_authors.type: user
paper_authors.user: annamonica
paper_authors._id: 682f037527c6b9d4500ded8e
paper_authors.name: Kan Wang
paper_authors._id: 682f037527c6b9d4500ded8f
paper_authors.name: Stephan Xie
paper_authors._id: 682f037527c6b9d4500ded90
paper_authors.name: David Asker
paper_authors._id: 682f037527c6b9d4500ded91
paper_authors.name: Ameet Talwalkar
paper_authors._id: 682f037527c6b9d4500ded92
paper_authors.name: Othmane Abou-Amal
paper_title: This Time is Different: An Observability Perspective on Time Series
    Foundation Models
paper_summary: We introduce Toto, a time series forecasting foundation model with 151
  million parameters. Toto uses a modern decoder-only architecture coupled with
  architectural innovations designed to account for specific challenges found in
  multivariate observability time series data. Toto's pre-training corpus is a
  mixture of observability data, open datasets, and synthetic data, and is
  4-10times larger than those of leading time series foundation models.
  Additionally, we introduce BOOM, a large-scale benchmark consisting of 350
  million observations across 2,807 real-world time series. For both Toto and
  BOOM, we source observability data exclusively from Datadog's own telemetry and
  internal observability metrics. Extensive evaluations demonstrate that Toto
  achieves state-of-the-art performance on both BOOM and on established general
  purpose time series forecasting benchmarks. Toto's model weights, inference
  code, and evaluation scripts, as well as BOOM's data and evaluation code, are
  all available as open source under the Apache 2.0 License available at
  https://huggingface.co/Datadog/Toto-Open-Base-1.0 and
  https://github.com/DataDog/toto.
paper_ai_summary: Toto is a time series forecasting foundation model using a decoder-only architecture, demonstrating state-of-the-art performance on large-scale benchmarks with multivariate observability data.
paper_ai_keywords: decoder-only architecture time series forecasting pre-training corpus observability data open datasets synthetic data BOOM real-world time series state-of-the-art performance general purpose time series forecasting benchmarks model weights inference code evaluation scripts