NaiveForecaster

class NaiveForecaster(strategy='last', window_length=None, sp=1)[source]

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.

Parameters:

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.
“mean”: (robust against NaN values)
forecast the mean of last window of training series when sp is 1. When sp is not 1, mean of all values in a season from last window will be forecasted for each season.
“drift”: (not robust against NaN values)
forecast by fitting a line between the first and last point of the window and extrapolating it into the future.

window_lengthint or None, default=None

Window length to use in the mean strategy. If None, entire training: series will be used.

spint, or None, default=1

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

Attributes:

cutoff: Cut-off = “present time” state of forecaster.
fh: Forecasting horizon that was passed.
is_fitted: Whether fit has been called.
state: State of the estimator.

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.

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])

Methods

`check_is_fitted`([method_name])	Check if the estimator has been fitted.
`clone`()	Obtain a clone of the object with same hyper-parameters and config.
`clone_tags`(estimator[, tag_names])	Clone tags from another object as dynamic override.
`create_test_instance`([parameter_set])	Construct an instance of the class, using first test parameter set.
`create_test_instances_and_names`([parameter_set])	Create list of all test instances and a list of names for them.
`fit`(y[, X, fh])	Fit forecaster to training data.
`fit_predict`(y[, X, fh, X_pred])	Fit and forecast time series at future horizon.
`get_class_tag`(tag_name[, tag_value_default])	Get class tag value from class, with tag level inheritance from parents.
`get_class_tags`()	Get class tags from class, with tag level inheritance from parent classes.
`get_config`()	Get config flags for self.
`get_fitted_params`([deep])	Get fitted parameters.
`get_param_defaults`()	Get object's parameter defaults.
`get_param_names`([sort])	Get object's parameter names.
`get_params`([deep])	Get a dict of parameters values for this object.
`get_pretrained_params`([deep])	Get pretrained parameters of this estimator.
`get_tag`(tag_name[, tag_value_default, ...])	Get tag value from instance, with tag level inheritance and overrides.
`get_tags`()	Get tags from instance, with tag level inheritance and overrides.
`get_test_params`([parameter_set])	Return testing parameter settings for the estimator.
`is_composite`()	Check if the object is composed of other BaseObjects.
`load_from_path`(serial)	Load object from file location.
`load_from_serial`(serial)	Load object from serialized memory container.
`predict`([fh, X])	Forecast time series at future horizon.
`predict_interval`([fh, X, coverage])	Compute/return prediction interval forecasts.
`predict_proba`([fh, X, marginal])	Compute/return fully probabilistic forecasts.
`predict_quantiles`([fh, X, alpha])	Compute/return quantile forecasts.
`predict_residuals`([y, X])	Return residuals of time series forecasts.
`predict_var`([fh, X, cov])	Compute/return variance forecasts.
`pretrain`(y[, X, fh])	Pre-train forecaster on panel (global) data.
`reset`()	Reset the object to a clean post-init state.
`save`([path, serialization_format])	Save serialized self to bytes-like object or to (.zip) file.
`score`(y[, X, fh])	Scores forecast against ground truth, using MAPE (non-symmetric).
`set_config`(**config_dict)	Set config flags to given values.
`set_params`(**params)	Set the parameters of this object.
`set_random_state`([random_state, deep, ...])	Set random_state pseudo-random seed parameters for self.
`set_tags`(**tag_dict)	Set instance level tag overrides to given values.
`update`(y[, X, update_params])	Update cutoff value and, optionally, fitted parameters.
`update_predict`(y[, cv, X, update_params, ...])	Make predictions and update model iteratively over the test set.
`update_predict_single`([y, fh, X, update_params])	Update model with new data and make forecasts.