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V0.0.19

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.

Full Changelog: https://github.com/AzulGarza/timecopilot/compare/v0.0.18...v0.0.19