V0.0.12

Features

• Query Method: Added a query method to the forecaster for flexible, programmatic access to model capabilities. See #134.

  from timecopilot import TimeCopilot
  
  tc = TimeCopilot(llm="openai:gpt-4o")
  tc.forecast(
      df="https://timecopilot.s3.amazonaws.com/public/data/air_passengers.csv", 
      h=12,
  )
  result = tc.query("What is the best model for monthly data?")
  print(result.output)
  

• Async TimeCopilot Agent: Introduced the AsyncTimeCopilot class for asynchronous forecasting and querying. See #135 and #138.

  import asyncio
  from timecopilot import AsyncTimeCopilot
  
  async def main():
      tc = AsyncTimeCopilot(llm="openai:gpt-4o")
      await tc.forecast(
          df="https://timecopilot.s3.amazonaws.com/public/data/air_passengers.csv",
          h=12
      )
      answer = await tc.query("Which model performed best?")
      print(answer.output)
  
  asyncio.run(main())
  

• Fallback Model Support: The TimeCopilotForecaster now supports a fallback model, which is used if the primary model fails. See #123.

  from timecopilot.forecaster import TimeCopilotForecaster
  from timecopilot.models.foundational.timesfm import TimesFM
  from timecopilot.models.benchmarks.stats import SeasonalNaive
  
  forecaster = TimeCopilotForecaster(
      models=[TimesFM()],
      fallback_model=SeasonalNaive()
  )
  

• TimesFM 2.0 Support: Added support for TimesFM 2.0, enabling the use of the latest version of Google's TimesFM model. See #128.

  from timecopilot.models.foundational.timesfm import TimesFM
  
  model = TimesFM(
      # default value
      repo_id="google/timesfm-2.0-500m-pytorch",
  )
  

• TabPFN Foundation Model: Added the TabPFN time series foundation model. See #113.

  import pandas as pd
  from timecopilot.models.foundational.tabpfn import TabPFN
  
  df = pd.read_csv("https://timecopilot.s3.amazonaws.com/public/data/algeria_exports.csv", parse_dates=["ds"])
  model = TabPFN()
  fcst = model.forecast(df, h=12)
  print(fcst)
  

• Median Ensemble: Introduced a new Median Ensemble model that combines predictions from multiple models to improve forecast accuracy. See #144.

  import pandas as pd
  from timecopilot.models.benchmarks import SeasonalNaive
  from timecopilot.models.ensembles.median import MedianEnsemble
  from timecopilot.models.foundational.chronos import Chronos
  
  
  df = pd.read_csv(
      "https://timecopilot.s3.amazonaws.com/public/data/air_passengers.csv",
      parse_dates=["ds"],
  )
  
  models = [
      Chronos(
          repo_id="amazon/chronos-t5-tiny",
          alias="Chronos-T5",
      ),
      Chronos(
          repo_id="amazon/chronos-bolt-tiny",
          alias="Chronos-Bolt",
      ),
      SeasonalNaive(),
  ]
  median_ensemble = MedianEnsemble(models=models)
  fcst_df = median_ensemble.forecast(
      df=df,
      h=12,
  )
  print(fcst_df)
  

• GIFTEval Module: Added the GIFTEval module for advanced evaluation of forecasting models. See #140.

  import pandas as pd
  from timecopilot.gift_eval.eval import GIFTEval, QUANTILE_LEVELS
  from timecopilot.gift_eval.gluonts_predictor import GluonTSPredictor
  from timecopilot.models.benchmarks import SeasonalNaive
  
  storage_path = ".pytest_cache/gift_eval"
  GIFTEval.download_data(storage_path)
  
  gifteval = GIFTEval(
      dataset_name="m4_weekly",
      term="short",
      output_path="./seasonal_naive",
      storage_path=storage_path,
  )
  predictor = GluonTSPredictor(
      forecaster=SeasonalNaive(),
      h=gifteval.dataset.prediction_length,
      freq=gifteval.dataset.freq,
      quantiles=QUANTILE_LEVELS,
      batch_size=512,
  )
  gifteval.evaluate_predictor(
      predictor,
      batch_size=512,
  )
  eval_df = pd.read_csv("./seasonal_naive/all_results.csv")
  print(eval_df)
  

Fixes

• Model Compatibility: Added support for the Moirai and TimeGPT models. See #115, #117.
• GluonTS Forecaster: Improved frequency handling and now uses the median for forecasts. See #124, #127.
• TimesFM Quantile Names: TimesFM now returns correct quantile names. See #131.
• Removed Lag Llama: The Lag Llama model has been removed. See #116.
• DataFrame Handling: Fixed DataFrame copying to avoid index side effects. See #120.

Docs

• Foundation Model Documentation: Added comprehensive documentation for foundation models, including paper citations and repository links. See #118.
• Unique Alias Validation: Added validation to prevent column conflicts in TimeCopilotForecaster. See #122.

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Full Changelog: https://github.com/AzulGarza/timecopilot/compare/v0.0.11...v0.0.12