Features

• IBM's FlowState foundational model: FlowState has been added to the foundational models hub. FlowState is the first time-scale adjustable Time Series Foundation Model (TSFM), combining a State Space Model (SSM) Encoder with a Functional Basis Decoder for time-scale invariant forecasting. Refer to #234 for more details.

  import pandas as pd
  from timecopilot.models.foundation.flowstate import FlowState
  df = pd.read_csv(
   "https://timecopilot.s3.amazonaws.com/public/data/events_pageviews.csv",
   parse_dates=["ds"],
  )
  # Use the commercial model
  model = FlowState(repo_id="ibm-granite/granite-timeseries-flowstate-r1")
  # Or use the research model
  # model = FlowState(repo_id="ibm-research/flowstate")
  fcst = model.forecast(df, h=12)
  print(fcst)

Fixes

• TimesFM 2.5 integration: Fixed compatibility issues with TimesFM 2.5 that were introduced in recent updates. The implementation now properly handles the new API changes in TimesFM 2.5. See #235.

• TimesFM loading from local path: Fixed an issue where TimesFM models couldn't be loaded from local file paths. The model loading mechanism has been updated to properly handle both local and remote model sources. Thanks to @KilianZimmerer for the contribution! See #230.

New Contributors

• @KilianZimmerer made their first contribution in #230

Full Changelog: AzulGarza/timecopilot@v0.0.19...v0.0.20