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MAPAForecaster

API referenceSource
Categorical in XInsamplePred int insampleExogenous

MAPAForecaster implements the Multiple Aggregation Prediction Algorithm (MAPA).

Quickstart

python
from sktime.forecasting.mapa import MAPAForecaster

estimator = MAPAForecaster(aggregation_levels=None, base_forecaster=None, agg_method='mean', decompose_type='multiplicative', forecast_combine='mean', imputation_method='ffill', sp=6, weights=None)

Parameters(8)

aggregation_levelslist of int, default=None

The levels at which the time series will be aggregated. If None, the levels will default to [1, 2, 4].

For example, with daily data:

  • Level 1: Original daily data

  • Level 2: Aggregate every 2 days

  • Level 4: Aggregate every 4 days

Lower levels capture short-term patterns while higher levels capture trends.

base_forecastersktime-compatible forecaster, default=None

The forecasting model to be used for each aggregation level.

If None, defaults to:

  • ExponentialSmoothing(trend=”add”, seasonal=”add”, sp=sp) if statsmodel present

  • NaiveForecaster(strategy=”mean”) if statsmodel not present

agg_methodstr, default=”mean”

Method used to aggregate the time series at different temporal levels.

Options are:

  • “mean”: Takes average of the periods (e.g., average of each 2-day period)

  • “sum”: Sums the values (useful for additive measures like sales)

decompose_typestr, default=”multiplicative”

The type of decomposition used in time series decomposition.

Options are:

  • “additive”: Components are added (trend + seasonal + residual)

  • “multiplicative”: Components are multiplied (trend * seasonal * residual)

forecast_combinestr, default=”mean”

Method used to combine the forecasts from different aggregation levels.

Options are:

  • “mean”: Simple average of all forecasts

  • “median”: Takes the median forecast

  • “weighted_mean”: Uses supplied weights for weighted average

imputation_methodstr, default=”ffill”

Method used for imputing missing values in the time series.

Options include:

  • “ffill”: Forward fill (propagate last valid observation forward)

  • “bfill”: Backward fill (use next valid observation)

  • “interpolate”: Linear interpolation between valid observations

spint, default=6
Seasonal periodicity of the time series.
weightslist of float, default=None
Optional weights to apply when combining forecasts. Only used if forecast_combine=”weighted_mean”. Must have same length as aggregation_levels. Weights are normalized to sum to 1.