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Forecaster

NaiveForecaster

API referenceSource
Categorical in XInsamplePred intPred int insampleMissing values

Forecast based on naive assumptions about past trends continuing.

NaiveForecaster is a forecaster that makes forecasts using simple strategies. Two out of three strategies are robust against NaNs. The NaiveForecaster can also be used for multivariate data and it then applies internally the ColumnEnsembleForecaster, so each column is forecasted with the same strategy.

Internally, this forecaster does the following:

  • obtains the so-called “last window”, a 1D array that denotes the most recent time window that the forecaster is allowed to use

  • reshapes the last window into a 2D array according to the given seasonal periodicity (prepended with NaN values to make it fit);

  • make a prediction for each column, using the given strategy:

    • “last”: last non-NaN row

    • “mean”: np.nanmean over rows

  • tile the predictions using the seasonal periodicity

To compute prediction quantiles, we first estimate the standard error of prediction residuals under the assumption of uncorrelated residuals. The forecast variance is then computed by multiplying the residual variance by a constant. This constant is a small-sample bias adjustment and each method (mean, last, drift) have different formulas for computing the constant. These formulas can be found in the Forecasting: Principles and Practice textbook (Table 5.2) [1]. Lastly, under the assumption that residuals follow a normal distribution, we use the forecast variance and z-scores of a normal distribution to estimate the prediction quantiles.

Quickstart

python
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]

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