title: Toto: Time Series Optimized Transformer for Observability
summary: This technical report describes the Time Series Optimized Transformer for
  Observability (Toto), a new state of the art foundation model for time series
  forecasting developed by Datadog. In addition to advancing the state of the art
  on generalized time series benchmarks in domains such as electricity and
  weather, this model is the first general-purpose time series forecasting
  foundation model to be specifically tuned for observability metrics.
    Toto was trained on a dataset of one trillion time series data points, the
  largest among all currently published time series foundation models. Alongside
  publicly available time series datasets, 75% of the data used to train Toto
  consists of fully anonymous numerical metric data points from the Datadog
  platform.
    In our experiments, Toto outperforms existing time series foundation models
  on observability data. It does this while also excelling at general-purpose
  forecasting tasks, achieving state-of-the-art zero-shot performance on multiple
  open benchmark datasets.
paper_authors._id: 669533cff5481f2dcd00dcef
paper_authors.name: Ben Cohen
paper_authors._id: 669533cff5481f2dcd00dcf0
paper_authors.name: Emaad Khwaja
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-07-16T20:16:20.146Z
paper_authors._id: 645e9d6d9c8e15af60a7d44f
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/645e9d6d9c8e15af60a7d44f/uCuZRH2YcYktidW-Re9Xp.png
paper_authors.fullname: Emaad Khwaja
paper_authors.type: user
paper_authors.user: Emaad
paper_authors._id: 669533cff5481f2dcd00dcf1
paper_authors.name: Kan Wang
paper_authors._id: 669533cff5481f2dcd00dcf2
paper_authors.name: Charles Masson
paper_authors._id: 669533cff5481f2dcd00dcf3
paper_authors.name: Elise Ramé
paper_authors._id: 669533cff5481f2dcd00dcf4
paper_authors.name: Youssef Doubli
paper_authors._id: 669533cff5481f2dcd00dcf5
paper_authors.name: Othmane Abou-Amal
paper_title: Toto: Time Series Optimized Transformer for Observability
paper_summary: This technical report describes the Time Series Optimized Transformer for
  Observability (Toto), a new state of the art foundation model for time series
  forecasting developed by Datadog. In addition to advancing the state of the art
  on generalized time series benchmarks in domains such as electricity and
  weather, this model is the first general-purpose time series forecasting
  foundation model to be specifically tuned for observability metrics.
    Toto was trained on a dataset of one trillion time series data points, the
  largest among all currently published time series foundation models. Alongside
  publicly available time series datasets, 75% of the data used to train Toto
  consists of fully anonymous numerical metric data points from the Datadog
  platform.
    In our experiments, Toto outperforms existing time series foundation models
  on observability data. It does this while also excelling at general-purpose
  forecasting tasks, achieving state-of-the-art zero-shot performance on multiple
  open benchmark datasets.
paper_ai_summary: Toto, a Time Series Optimized Transformer for Observability, achieves state-of-the-art performance in observability and general-purpose forecasting using a vast dataset of time series data.
paper_ai_keywords: Time Series Optimized Transformer observability time series forecasting foundation model time series data zero-shot performance