multiseries

If forecasting with many series in a loop, these functions and code examples may facilitate setting up the process and getting key information for each model and series.

export_model_summaries()

scalecast.multiseries.export_model_summaries(f_dict: dict[str, Forecaster], **kwargs: Any) → DataFrame

Exports a pandas dataframe with information about each model run on each eries when doing forecasting using many different series.

Parameters:

• f_dict (dict[str,Forecaster]) – Dictionary of forcaster objects.

• **kwargs – Passed to the Forecaster.export() function (do not pass dfs arg as that is set automatically to ‘model_summaries’).

Returns:

(DataFrame) The combined model summaries.

from scalecast.Forecaster import Forecaster
from scalecast import GridGenerator
from scalecast.multiseries import export_model_summaries
import pandas_datareader as pdr

f_dict = {}
models = ('mlr','elasticnet','mlp')
GridGenerator.get_example_grids() # writes the Grids.py file to your working directory

for sym in ('UNRATE','GDP'):
  df = pdr.get_data_fred(sym, start = '2000-01-01')
  f = Forecaster(
    y=df[sym],
    current_dates=df.index,
    future_dates = 12,
    test_length = .1,
    validation_length = 12,
  )
  f.add_ar_terms(12)
  f.add_time_trend()
  f.tune_test_forecast(models)
  f_dict[sym] = f

model_summaries = export_model_summaries(f_dict,determine_best_by='LevelTestSetMAPE')

line_up_dates()

scalecast.multiseries.line_up_dates(*fs: Unpack[Forecaster]) → tuple[Unpack[Forecaster]]

Trims all passed Forecaster objects so they all have the same dates.

Parameters:

*fs (Forecaster objects) – The objects to check and trim.

Returns:

Forecaster objects with the same dates.

keep_smallest_first_date()

scalecast.multiseries.keep_smallest_first_date(*fs: Unpack[Forecaster]) → tuple[Unpack[Forecaster]]

Trims all passed Forecaster objects so they all have the same first date.

Parameters:

*fs (Forecaster objects) – The objects to check and trim.

Returns:

Forecaster objects with the same first date.