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Exponential Smoothing ETS

Creates a generic ETS forecast with some seasonal values already filled in.

https://www.statsmodels.org/dev/generated/statsmodels.tsa.exponential_smoothing.ets.ETSModel.html

from scripts.helpers.time_series_helpers import forecast_ets
def forecast_ets(
  dataframe: pd.DataFrame,
  start_date: str,
  end_date: str,
  seasonal_period: str = "none",
  error: str = "add",
  trend: str = "add",
  damped_trend: bool = False):
   
  Returns:
    Forecast Results (Dataframe)

Required Arguements:

  • Dataframe (dataframe): Dataframe containing training timeseries dataset.
  • start_date (string): Start date of the Forecast,
  • end_date (string): End date of the Forecast
  • seasonal_period (String) Optional: Period for the data. "M" / "W" / "Q" this will fill in the parameters for the model based on very commonly used values, not including days. Default is "none", use "none" if the data is not seasonal

Optional Arguements:

  • error (String): The error model. “add” (default) or “mul”.
  • trend (String): The trend component model. “add” (default), “mul”, or None.
  • damped_trend (Bool): Whether an included trend component is damped. Default is False.

For example, if you have monthly data, it is quite common to want to check for yearly seasonality, simply doing the following will give you a forecast

forecast = forecast_ets(df,start_date,end_date,"M")

ETS Forecast using seasonality set to 12 for months in a year