v0.0.19

[AzulGarza]

Features

• Google's TimesFM 2.5 foundational model: TimesFM 2.5 has been added to the foundational models hub. Refer to #224 for more details.

  import pandas as pd
  from timecopilot.models.foundation.timesfm import TimesFM
  
  df = pd.read_csv(
      "https://timecopilot.s3.amazonaws.com/public/data/events_pageviews.csv",
      parse_dates=["ds"],
  )
  model = TimesFM(repo_id="google/timesfm-2.5-200m-pytorch")
  fcst = model.forecast(df, h=12)
  print(fcst)

• Improved agent prompt: Enhanced the agent prompt for better performance and accuracy. See #218.

Fixes

• Correct pydantic-ai library: Fixed the pydantic-ai library dependency to ensure proper functionality. See #221.

• TimesFM quantiles: Fixed quantile handling for TimesFM models to ensure correct probabilistic forecasts. See #225.

Documentation

• Time series foundation models comparison example: Added comprehensive example on how to compare time series foundational models. See #227.

• Revamped static site: Major improvements to the static documentation site with better design and navigation. See #226.

Infrastructure

• Documentation tests across Python versions: Added testing for documentation examples across multiple Python versions to ensure compatibility. See #220.

• S3 URL standardization: Updated to use S3 URLs instead of URIs for better consistency. See #222.

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Full Changelog: AzulGarza/timecopilot@v0.0.18...v0.0.19

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This discussion was created from the release v0.0.19.