release: v0.0.25 (#333)

[AzulGarza]

Features

• TimeGPT finetuning: Finetuning is now supported for TimeGPT. You can adapt the pre-trained model to your data before forecasting via TimeGPTFinetuningConfig, with options for loss function and finetuning depth. See #332 and the Finetuning Foundation Models example for a full walkthrough.

  import pandas as pd
  from timecopilot.models.foundation.timegpt import TimeGPT, TimeGPTFinetuningConfig
  
  df = pd.read_csv(
      "https://timecopilot.s3.amazonaws.com/public/data/events_pageviews.csv",
      parse_dates=["ds"],
  )
  
  model = TimeGPT(
      finetuning_config=TimeGPTFinetuningConfig(
          finetune_steps=10,
          finetune_loss="mse",
      ),
      alias="TimeGPT-finetuned",
  )

Documentation

• Finetuning evaluation example: Added a finetuning evaluation section to the Finetuning Foundation Models notebook, comparing MAPE across different finetune_steps values via cross-validation. See #326.

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Full Changelog: v0.0.24...v0.0.25

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This discussion was created from the release release: v0.0.25 (#333).