package com.imsl.test.example.stat;

import java.util.*;
import com.imsl.stat.*;

/**
 * <p>
 * Searches for the best fitting non-seasonal \(
 * \text{ARIMA} \).
 * </p>
 * <p>
 * This example uses time series LNU03327709 from the US Department of Labor,
 * Bureau of Labor Statistics. It contains the unadjusted special unemployment
 * rate, taken monthly from January 1994 through September 2005. The values
 * 01/2004 - 03/2005 are used by class <code>autoARIMA</code> for outlier
 * detection and parameter estimation. Technique 1, invoked by
 * <code>autoARIMA.compute(int maxlag)</code> is chosen to find an appropriate
 * \(\text{ARIMA}(p,d,0)\) model. That is, the algorithm searches for the best
 * fitting parameters while it is assumed that \(q = 0\). With the selected
 * model, forecasts are generated for the following six months and compared with
 * the actual values 04/2005 - 09/2005.
 * </p>
 *
 * @see <a href="AutoARIMAEx1.java">Code</a>
 * @see <a href="doc-files/AutoARIMAEx1.out">Output</a>
 */
public class AutoARIMAEx1 {

    public static void main(String args[]) throws Exception {
        int nOutliers;
        double aic, RSE, constant;
        int[] optimumModel;
        int[][] outlierStatistics;
        double[] outlierForecast, ar, ma;
        double[] psiWeights, probabilityLimits;
        double[] x = {
            12.8, 12.2, 11.9, 10.9, 10.6, 11.3, 11.1, 10.4, 10.0, 9.7, 9.7, 9.7,
            11.1, 10.5, 10.3, 9.8, 9.8, 10.4, 10.4, 10.0, 9.7, 9.3, 9.6, 9.7,
            10.8, 10.7, 10.3, 9.7, 9.5, 10.0, 10.0, 9.3, 9.0, 8.8, 8.9, 9.2,
            10.4, 10.0, 9.6, 9.0, 8.5, 9.2, 9.0, 8.6, 8.3, 7.9, 8.0, 8.2,
            9.3, 8.9, 8.9, 7.7, 7.6, 8.4, 8.5, 7.8, 7.6, 7.3, 7.2, 7.3,
            8.5, 8.2, 7.9, 7.4, 7.1, 7.9, 7.7, 7.2, 7.0, 6.7, 6.8, 6.9,
            7.8, 7.6, 7.4, 6.6, 6.8, 7.2, 7.2, 7.0, 6.6, 6.3, 6.8, 6.7,
            8.1, 7.9, 7.6, 7.1, 7.2, 8.2, 8.1, 8.1, 8.2, 8.7, 9.0, 9.3,
            10.5, 10.1, 9.9, 9.4, 9.2, 9.8, 9.9, 9.5, 9.0, 9.0, 9.4, 9.6,
            11.0, 10.8, 10.4, 9.8, 9.7, 10.6, 10.5, 10.0, 9.8, 9.5, 9.7, 9.6,
            10.9, 10.3, 10.4, 9.3, 9.3, 9.8, 9.8, 9.3, 8.9, 9.1, 9.1, 9.1,
            10.2, 9.9, 9.4
        };
        double[] exactForecast = {8.7, 8.6, 9.3, 9.1, 8.8, 8.5};
        int[] times = {
            1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
            13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24,
            25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,
            37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48,
            49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60,
            61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,
            73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84,
            85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96,
            97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108,
            109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120,
            121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132,
            133, 134, 135
        };

        AutoARIMA autoArima = new AutoARIMA(times, x);
        autoArima.setCriticalValue(3.8);
        autoArima.compute(5);
        autoArima.forecast(6);

        nOutliers = autoArima.getNumberOfOutliers();
        aic = autoArima.getAIC();
        optimumModel = autoArima.getOptimumModelOrder();
        outlierStatistics = autoArima.getOutlierStatistics();
        RSE = autoArima.getResidualStandardError();
        outlierForecast = autoArima.getForecast();
        psiWeights = autoArima.getPsiWeights();
        probabilityLimits = autoArima.getDeviations();
        constant = autoArima.getConstant();
        ar = autoArima.getAR();
        ma = autoArima.getMA();

        System.out.printf("%nMethod 1: Automatic AR model selection"
                + ", no differencing%n");
        System.out.printf("%nOptimum Model: p=%d, q=%d, s=%d, d=%d%n",
                optimumModel[0], optimumModel[1],
                optimumModel[2], optimumModel[3]);
        System.out.printf("%nNumber of outliers:%3d%n%n", nOutliers);
        System.out.printf("Outlier statistics:%n");
        System.out.printf(" Time%4sType%n", " ");
        for (int i = 0; i < nOutliers; i++) {
            System.out.printf("%5d%8d%n", outlierStatistics[i][0],
                    outlierStatistics[i][1]);
        }
        System.out.printf(Locale.ENGLISH, "%nAIC:%12.6f%n", aic);
        System.out.printf(Locale.ENGLISH, "RSE:%12.6f%n%n", RSE);
        System.out.printf("%6sParameters%n", " ");
        System.out.printf(Locale.ENGLISH, " constant:%12.6f%n", constant);
        for (int i = 0; i < ar.length; i++) {
            System.out.printf(Locale.ENGLISH, " ar[%d]:%15.6f%n", i, ar[i]);
        }
        for (int i = 0; i < ma.length; i++) {
            System.out.printf(Locale.ENGLISH, " ma[%d]:%15.6f%n", i, ma[i]);
        }

        System.out.printf("%n%n%6s* * * Forecast Table * * *%n", " ");
        System.out.printf("%2sExact%3sforecast%5slimits%8spsi%n",
                " ", " ", " ", " ");
        for (int i = 0; i < outlierForecast.length; i++) {
            System.out.printf(Locale.ENGLISH, "%7.4f%11.4f%11.4f%11.4f%n",
                    exactForecast[i], outlierForecast[i],
                    probabilityLimits[i], psiWeights[i]);
        }
    }
}