V0.0.13

Features

• Custom seasonalitites. Added custom_seasonalities argument to get_seasonality. See #147.

  from timecopilot.models.utils.forecaster import get_seasonality
  
  print(get_seasonality("D", custom_seasonalities={"D": 7}))
  # 7
  print(get_seasonality("D")) # default seasonalities are used
  # 1
  

• GIFTEval results concatenation. Added functionality to concatenate results for different datasets when GIFTEval(...).evaluate_predictor(...) is used. See #148.

  import pandas as pd
  from timecopilot.gift_eval.eval import GIFTEval
  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)
  
  predictor = GluonTSPredictor(
      forecaster=SeasonalNaive(),
      batch_size=512,
  )
  
  def evaluate_predictor(
      dataset_name: str,
      term: str,
      overwrite_results: bool = False,
  ):
      gifteval = GIFTEval(
          dataset_name=dataset_name,
          term=term,
          output_path="./seasonal_naive",
          storage_path=storage_path,
      )
      gifteval.evaluate_predictor(
          predictor,
          batch_size=512,
          overwrite_results=overwrite_results,
      )
  
  combinations = [
      ("m4_weekly", "short"),
      ("m4_hourly", "short"),
  ]
  
  for i, (dataset_name, term) in enumerate(combinations):
      evaluate_predictor(
          dataset_name=dataset_name,
          term=term,
      )
  eval_df = pd.read_csv("./seasonal_naive/all_results.csv")
  print(eval_df) # it includes eval for the two datasets
  
  # you can use overwrite_results to generate a new file of results 
  evaluate_predictor(
      dataset_name="m4_weekly",
      term="short",
      overwrite_results=True
  )
  eval_df = pd.read_csv("./seasonal_naive/all_results.csv")
  print(eval_df) # it includes eval just for m4_weekly
  

Refactorings

• GIFTEval Module: Refactored the GIFTEval module to infer horizon h and frequency freq directly from the dataset when calling GIFTEval(...).evaluate_predictor(...). See #147. New usage:

  import pandas as pd
  from timecopilot.gift_eval.eval import GIFTEval
  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)
  
  predictor = GluonTSPredictor(
      # you can use any forecaster from TimeCopilot
      # and create your own forecaster by subclassing 
      # [Forecaster][timecopilot.models.utils.forecaster.Forecaster]
      forecaster=SeasonalNaive(),
      batch_size=512,
  )
  gift_eval = GIFTEval(
      dataset_name="m4_daily",
      term="short",
      output_path="./seasonal_naive",
      storage_path=storage_path,
  )
  gift_eval.evaluate_predictor(
      predictor,
      batch_size=512,
  )
  eval_df = pd.read_csv("./seasonal_naive/all_results.csv")
  print(eval_df)
  

• Median ensemble: Now it applies median plus isotonic regression for probabilistic forecasts. See #149.

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Full Changelog: https://github.com/AzulGarza/timecopilot/compare/v0.0.12...v0.0.13