Hypertuning segment and period selection

Descriptions of the basic functions are given below.

Function descriptions:

class hyperparametertuning.HyperTunedAggregations(base_aggregation, saveAggregationHistory=True)

__init__(base_aggregation, saveAggregationHistory=True)

A class that does a parameter variation and tuning of the aggregation itself.

Parameters:

• base_aggregation (TimeSeriesAggregation) – TimeSeriesAggregation object which is used as basis for tuning the hyper parameters. required

• saveAggregationHistory (boolean) – Defines if all aggregations that are created during the tuning and iterations shall be saved under self.aggregationHistory.

identifyOptimalSegmentPeriodCombination(dataReduction)

Identifies the optimal combination of number of typical periods and number of segments for a given data reduction set.

Parameters:

dataReduction (float) – Factor by which the resulting dataset should be reduced. required

Returns:

noSegments, noTypicalperiods – The optimal combination of segments and typical periods for the given optimization set.

identifyParetoOptimalAggregation(untilTotalTimeSteps=None)

Identifies the pareto-optimal combination of number of typical periods and number of segments along with a steepest decent approach, starting from the aggregation to a single period and a single segment up to the representation of the full time series.

Parameters:

untilTotalTimeSteps (int) – Number of timesteps until which the pareto-front should be determined. If None, the maximum number of timesteps is chosen.

Returns:

**** – Nothing. Check aggregation history for results. All typical Periods in scaled form.