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.

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