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V0.0.18

Features

  • Anomaly Detection Capabilities: Added comprehensive anomaly detection functionality to the forecaster, enabling identification of outliers and unusual patterns in time series data. See #213.

    import pandas as pd
from timecopilot import TimeCopilotForecaster
from timecopilot.models.stats import SeasonalNaive, Theta
from timecopilot.models.foundation.chronos import Chronos

# Load your time series data
df = pd.read_csv(
    "https://timecopilot.s3.amazonaws.com/public/data/taylor_swift_pageviews.csv",
    parse_dates=["ds"],
)

# Create forecaster with multiple models
tcf = TimeCopilotForecaster(
    models=[
        Chronos(repo_id="amazon/chronos-bolt-mini"),
        SeasonalNaive(),
        Theta(),
    ]
)

# Detect anomalies with 95% confidence level
anomalies_df = tcf.detect_anomalies(df=df, h=7, level=95)

# Visualize the results
tcf.plot(df, anomalies_df)

  • fev Experiments: Added new fev experiments to expand the evaluation results. See #211.

  • Chat-like CLI Capabilities: Introduced an interactive, conversational CLI interface that enables natural language interaction with TimeCopilot. The CLI now supports seamless model switching, anomaly detection integration, and real-time plotting capabilities. See #215.

    # Start the interactive CLI
uv run timecopilot

# Natural conversation examples:
> "forecast the next 12 months"
> "now try this with Chronos"
> "highlight anomalies in this series"
> "show me the plot"
> "explain the results"

Fixes

  • GIFT-Eval Import Corrections: Fixed import statements after refactoring in the GIFT-Eval experiment to ensure proper functionality. See #209.

  • Documentation Link Updates: Corrected links throughout the documentation after the recent refactoring to maintain proper navigation. See #210.

Documentation

  • README Improvements: Enhanced README.md with updated information and improved clarity. See #207.

Full Changelog: https://github.com/AzulGarza/timecopilot/compare/v0.0.17...v0.0.18