Features

• Toto 2.0 support: The Toto class now transparently supports both Toto 1.0 and Toto 2.0 foundation models. Pass a Toto 2.0 checkpoint (e.g. Datadog/Toto-2.0-4m) as repo_id and the model family is detected automatically from the checkpoint configuration. Toto 2.0 predicts a fixed set of quantile knots (0.1, ..., 0.9): the median is used as the point forecast and requested quantiles/level are obtained by linear interpolation across the knots. See #343.

  import pandas as pd
  from timecopilot.models.foundation.toto import Toto
  df = pd.read_csv(
   "https://timecopilot.s3.amazonaws.com/public/data/air_passengers.csv",
   parse_dates=["ds"],
  )
  model = Toto(repo_id="Datadog/Toto-2.0-4m", alias="Toto-2")
  fcst_df = model.forecast(df, h=12, quantiles=[0.1, 0.5, 0.9])

  Available Toto 2.0 checkpoints: Datadog/Toto-2.0-4m, Datadog/Toto-2.0-22m, Datadog/Toto-2.0-313m, Datadog/Toto-2.0-1B, and Datadog/Toto-2.0-2.5B.

Documentation

• Toto family example: Added the Toto Family notebook, comparing Toto 1.0, Toto 2.0, Prophet, AutoARIMA, and SeasonalNaive. See #343.

Full Changelog: v0.0.26...v0.0.27