Features

• New neural models: Added 3 new auto neural models: AutoNBEATS, AutoDeepAR, and AutoPatchTST. All support quantiles for probabilistic forecasts trained with MQLoss and follow the same interface as the existing AutoNHITS and AutoTFT. See #338.

  import pandas as pd
  from timecopilot.models.neural import AutoDeepAR, AutoNBEATS, AutoPatchTST
  df = pd.read_csv(
   "https://timecopilot.s3.amazonaws.com/public/data/air_passengers.csv",
   parse_dates=["ds"],
  )
  model = AutoNBEATS()
  fcst_df = model.forecast(df, h=12, quantiles=[0.1, 0.5, 0.9])

• New ML models: Added 7 new auto ML models: AutoLinearRegression, AutoXGBoost, AutoRidge, AutoLasso, AutoElasticNet, AutoRandomForest, and AutoCatboost. All models support quantiles for probabilistic forecasts via conformal prediction and follow the same interface as the existing AutoLGBM. See #337.

  import pandas as pd
  from timecopilot.models.ml import (
   AutoLinearRegression,
   AutoXGBoost,
   AutoRidge,
   AutoLasso,
   AutoElasticNet,
   AutoRandomForest,
   AutoCatboost,
  )
  df = pd.read_csv(
   "https://timecopilot.s3.amazonaws.com/public/data/air_passengers.csv",
   parse_dates=["ds"],
  )
  model = AutoRidge()
  fcst_df = model.forecast(df, h=12, quantiles=[0.1, 0.5, 0.9])

• Quantile forecasts for AutoLGBM, AutoNHITS, and AutoTFT: These models now support quantile forecasts via the quantiles parameter. Pass a list of floats between 0 and 1 to receive additional output columns named model-q-{percentile}. Note that level is not supported for these models; use quantiles instead. See #336.

  • AutoLGBM computes prediction intervals via conformal prediction using cross-validation residuals.
  • AutoNHITS and AutoTFT are trained with MQLoss when quantiles are requested.

  import pandas as pd
  from timecopilot.models.ml import AutoLGBM
  from timecopilot.models.neural import AutoNHITS, AutoTFT
  df = pd.read_csv(
   "https://timecopilot.s3.amazonaws.com/public/data/air_passengers.csv",
   parse_dates=["ds"],
  )
  model = AutoLGBM()
  fcst_df = model.forecast(df, h=12, quantiles=[0.1, 0.5, 0.9])
  # columns: unique_id, ds, AutoLGBM, AutoLGBM-q-10, AutoLGBM-q-50, AutoLGBM-q-90

Documentation

• Custom ensembles example: Added the Custom Ensembles notebook, showing how to combine multiple models into custom ensembles. See #340.

• Explaining foundation models and ensembles example: Added the Explaining Foundation Models and Ensembles notebook. See #340.

Full Changelog: v0.0.25...v0.0.26