release: v0.1.3

[AzulGarza]

Features

• TimesFM 3.0 foundation model: Added support for google/timesfm-3.0-pytorch via a new _TimesFMV3 backend powered by TimesFM3Evaluator and ModelConfig from timecopilot-timesfm>=0.4.0. The wrapper treats each series independently (univariate panel mode), matching the existing TimesFM 2.5 integration pattern.

  import pandas as pd
  from foundationforecast import TimesFM
  
  df = pd.read_csv(
      "https://timecopilot.s3.amazonaws.com/public/data/air_passengers.csv",
      parse_dates=["ds"],
  )
  
  model = TimesFM(
      repo_id="google/timesfm-3.0-pytorch",
      alias="TimesFM-3",
      context_length=2048,
  )
  fcst_df = model.forecast(df, h=12, freq="MS")

  License note: TimesFM 3.0 pretrained weights are non-commercial; TimesFM 1.0–2.5 checkpoints remain Apache-2.0.

• GIFT-Eval: Registered google--timesfm-3.0-pytorch (reference slug TimesFM-3) in the gift-eval model registry and added it to the CI replication subset on m4_weekly/short.

• TimesFM family notebook: Added timesfm-family example notebook comparing TimesFM 1.0, 2.0, 2.5, and 3.0 with supported prediction intervals.

Dependencies

• Bumped timecopilot-timesfm from >=0.3.0 to >=0.4.0.

Documentation

• Updated README, model hub, and TimesFM docstrings with TimesFM 3.0 support and non-commercial license citation.

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Full Changelog: v0.1.2...v0.1.3

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This discussion was created from the release release: v0.1.3.