Package org.elasticsearch.search.aggregations.pipeline

Class SimulatedAnealingMinimizer

java.lang.Object
org.elasticsearch.search.aggregations.pipeline.SimulatedAnealingMinimizer
public class SimulatedAnealingMinimizer
extends java.lang.Object
A cost minimizer which will fit a MovAvgModel to the data. This optimizer uses naive simulated annealing. Random solutions in the problem space are generated, compared against the last period of data, and the least absolute deviation is recorded as a cost. If the new cost is better than the old cost, the new coefficients are chosen. If the new solution is worse, there is a temperature-dependent probability it will be randomly selected anyway. This allows the algo to sample the problem space widely. As iterations progress, the temperature decreases and the algorithm rejects poor solutions more regularly, theoretically honing in on a global minimum.

  • Constructor Summary

    Constructors 
    Constructor Description
    SimulatedAnealingMinimizer()  

  • Method Summary

    Modifier and Type Method Description
    static MovAvgModel minimize​(MovAvgModel model, EvictingQueue<java.lang.Double> train, double[] test)
    Runs the simulated annealing algorithm and produces a model with new coefficients that, theoretically fit the data better and generalizes to future forecasts without overfitting.

    Methods inherited from class java.lang.Object

    clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

  • Constructor Details

  • Method Details

    • minimize

      public static MovAvgModel minimize​(MovAvgModel model, EvictingQueue<java.lang.Double> train, double[] test)
      Runs the simulated annealing algorithm and produces a model with new coefficients that, theoretically fit the data better and generalizes to future forecasts without overfitting.
      Parameters:
      model - The MovAvgModel to be optimized for
      train - A training set provided to the model, which predictions will be generated from
      test - A test set of data to compare the predictions against and derive a cost for the model
      Returns:
      A new, minimized model that (theoretically) better fits the data