Package org.elasticsearch.search.aggregations.pipeline.movavg

Class 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.

    • 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 Detail

      • SimulatedAnealingMinimizer

        public SimulatedAnealingMinimizer()

    • Method Detail

      • 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