Toto: Time Series Optimized Transformer for Observability
Authors: name: Ben Cohen, name: Emaad Khwaja, name: Kan Wang, name: Charles Masson, name: Elise Ramé, name: Youssef Doubli, name: Othmane Abou-Amal
Organization: Datadog

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.