NaiveForecaster

Forecast based on naive assumptions about past trends continuing.

Quickstart

from sktime.forecasting.naive import NaiveForecaster

estimator = NaiveForecaster(strategy='last', window_length=None, sp=1)

Parameters(3)

strategy{“last”, “mean”, “drift”}, default=”last”

Strategy used to make forecasts:

• “last”: (robust against NaN values)

  forecast the last value in the training series when sp is 1. When sp is not 1, last value of each season in the last window will be forecasted for each season.

spint, or None, default=1

Seasonal periodicity to use in the seasonal forecasting. None=1.

window_lengthint or None, default=None

Window length to use in the mean strategy. If None, entire training

series will be used.

Examples

>>> from sktime.datasets import load_airline
>>> from sktime.forecasting.naive import NaiveForecaster
>>> y = load_airline ()
>>> forecaster = NaiveForecaster (strategy = "drift")
>>> forecaster. fit (y) NaiveForecaster(
... )
>>> y_pred = forecaster. predict (fh = [1, 2, 3 ])
>>> 
>>> # Example 2: Seasonal Naive strategy
>>> # The airline data is monthly, so we use sp=12 (12 months per year)
>>> forecaster = NaiveForecaster (strategy = "last", sp = 12)
>>> forecaster. fit (y) NaiveForecaster(sp=12)
>>> y_pred = forecaster. predict (fh = [1, 2, 3 ])

References

1. [1 ] Hyndman, R.J., & Athanasopoulos, G. (2021) Forecasting: principles and practice, 3rd edition, OTexts: Melbourne, Australia. OTexts.com/fpp3. Accessed on 22 September 2022.