com.opengamma.analytics.financial.timeseries.analysis.SampleAutocorrelationIIDHypothesis.java Source code

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Here is the source code for com.opengamma.analytics.financial.timeseries.analysis.SampleAutocorrelationIIDHypothesis.java

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/**
 * Copyright (C) 2009 - present by OpenGamma Inc. and the OpenGamma group of companies
 * 
 * Please see distribution for license.
 */
package com.opengamma.analytics.financial.timeseries.analysis;

import org.apache.commons.lang.Validate;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import com.opengamma.analytics.math.function.Function1D;
import com.opengamma.analytics.math.statistics.distribution.NormalDistribution;
import com.opengamma.analytics.math.statistics.distribution.ProbabilityDistribution;
import com.opengamma.timeseries.DoubleTimeSeries;
import com.opengamma.util.ArgumentChecker;

/**
 * 
 */
public class SampleAutocorrelationIIDHypothesis extends IIDHypothesis {
    private static final Logger s_logger = LoggerFactory.getLogger(SampleAutocorrelationIIDHypothesis.class);
    private final Function1D<DoubleTimeSeries<?>, double[]> _calculator = new AutocorrelationFunctionCalculator();
    private final double _level;
    private final double _criticalValue;
    private final int _h;

    public SampleAutocorrelationIIDHypothesis(final double level, final int maxLag) {
        if (!ArgumentChecker.isInRangeExcludingLow(0, 1, level)) {
            throw new IllegalArgumentException("Level must be between 0 and 1");
        }
        if (maxLag == 0) {
            throw new IllegalArgumentException("Lag cannot be zero");
        }
        if (maxLag < 0) {
            s_logger.warn("Maximum lag was less than zero; using absolute value");
        }
        _level = level;
        final ProbabilityDistribution<Double> normal = new NormalDistribution(0, 1);
        _criticalValue = normal.getInverseCDF(1 - level / 2.);
        _h = maxLag;
    }

    @Override
    public boolean testIID(final DoubleTimeSeries<?> x) {
        Validate.notNull(x, "x");
        if (x.size() < _h) {
            throw new IllegalArgumentException("Time series must have at least " + _h + " points");
        }
        final double[] autocorrelations = _calculator.evaluate(x);
        final double upper = _criticalValue / Math.sqrt(x.size());
        final double lower = -upper;
        double violations = 0;
        double value;
        for (int i = 1; i < _h; i++) {
            value = autocorrelations[i];
            if (value > upper || value < lower) {
                violations++;
            }
        }
        if (violations / _h > _level) {
            return false;
        }
        return true;
    }
}