v0.0.17

[AzulGarza]

Features

• Moirai2 Foundation Model: Added support for the Moirai2 model, a new state-of-the-art foundation model for time series forecasting. See #177.

  import pandas as pd
  from timecopilot.models.foundation.moirai import Moirai
  
  df = pd.read_csv(
      "https://timecopilot.s3.amazonaws.com/public/data/air_passengers.csv", 
      parse_dates=["ds"]
  )
  model = Moirai(repo_id="Salesforce/moirai-2.0-R-small")
  fcst = model.forecast(df, h=12)
  print(fcst)

• Machine Learning and Neural Forecasting Methods: Expanded the forecasting capabilities with new ML and neural methods including AutoLightGBM, AutoNHITS y AutoTFT. See #181.

• Static Plot Method: Added a static plotting method for visualizing forecasts without requiring an agent instance. See #183.

  import pandas as pd
  from timecopilot import TimeCopilotForecaster
  from timecopilot.models.foundation.moirai import Moirai
  from timecopilot.models.prophet import Prophet
  from timecopilot.models.stats import AutoARIMA, AutoETS, SeasonalNaive
  
  df = pd.read_csv(
      "https://timecopilot.s3.amazonaws.com/public/data/air_passengers.csv",
      parse_dates=["ds"],
  )
  tcf = TimeCopilotForecaster(
      models=[
          AutoARIMA(), 
          AutoETS(), 
          Moirai(), 
          Prophet(), 
          SeasonalNaive(),
      ]
  )
  fcst_df = tcf.forecast(df=df, h=12, level=[80, 90])
  tcf.plot(df, fcst_df, level=[80, 90])

• Enhanced Documentation with Examples: Added comprehensive examples section using mkdocs-jupyter, including interactive notebooks for agent quickstart and forecaster usage. See #176 and #198.

• GIFT-Eval Plotting: Added plots for the GIFT-Eval experiment to better visualize model performance across different datasets. See #180.

• Improved Date and Target Column Handling: Specify to the agent the handling of date (ds) and target (y) columns. See #139.

Refactorings

• Clearer Models Structure: Reorganized the models module for better clarity and maintainability. Models are now organized into logical categories: stats, ml, neural, foundation, and ensembles. See #203.

  • Prophet moved from models.benchmarks.prophet to models.prophet
  • Statistical models moved from models.benchmarks.stats to models.stats
  • ML models moved from models.benchmarks.ml to models.ml
  • Neural models moved from models.benchmarks.neural to models.neural

• Improved DataFrame Concatenation: Optimized DataFrame concatenation in feature extraction loops for better performance. See #105.

Fixes

• OpenAI Version Compatibility: Unpinned OpenAI version to resolve compatibility issues with recent releases. See #171.

• Median Ensemble Level Test: Relaxed test constraints for median ensemble levels to improve test reliability. See #175.

• Documentation URL Format: Updated documentation to use kebab-case URLs for better consistency. See #200.

• Explicit Keyword Arguments: Added explicit override handling for keyword arguments to prevent unexpected behavior. See #202.

Documentation

• Enhanced README: Improved README content with additional information and fixed various typos. See #172, #187, #188.

• New Logo and Branding: Added new logos and favicon for improved visual identity. See #185, #186.

• Issue Templates: Added GitHub issue templates to streamline bug reporting and feature requests. See #193.

• Documentation Testing: Added comprehensive tests for documentation to ensure code examples work correctly. See #194.

Infrastructure

• CI/CD Improvements: Moved linting action to the main CI workflow for better organization. See #174.

• Discord Release Notifications: Added automated Discord notifications for new releases. See #195, #196, #197.

• Improved Experiment Naming: Better naming conventions for GIFT-Eval experiments. See #199.

New Contributors

• @elmartinj made their first contribution in #187
• @friscobrisco made their first contribution in #139

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Full Changelog: AzulGarza/timecopilot@v0.0.16...v0.0.17

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This discussion was created from the release v0.0.17.