Example 3: Forecasting an outlier contaminated time series

This example is a realization of an \text{ARMA}(2,1) process described by the model Y_t-Y_{t-1}+0.24Y_{t-2} = 10.0+a_t+0.5a_{t-1},\, \{a_t\} a Gaussian White noise process. An additive outlier with \omega_1=4.5 was added at time point t=150, a temporary change outlier with \omega_2=3.0 was added at time point t=200.

Outliers were artificially added to the outlier free series \{Y-t\}_{t=1,\ldots, 280} at time points t=150 (level shift with \omega_1=+2.5) and t=200 (additive outlier with \omega_2=+3.2), resulting in the outlier contaminated series \{Z_t\}_{t=1,\ldots,280}. For both series, forecasts were determined for time points t=281,\ldots,290 and compared with the actual values of the series.

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

public class ARMAOutlierIdentificationEx3
{
  public static void main(String args[]) throws Exception {

    double resStdErr, aic;
    int[][] outlierStatistics;
    int numOutliers;
    int[] model = new int[4];
    double constant;
    double[] ar, ma;
    int nForecast = 10;
	double[] outlierContaminatedForecast;
	double[] outlierFreeForecast;
	double[] probabilityLimits;
	double[] psiWeights;
    

    double[] outlierContaminatedSeries ={
      41.6699982,41.6699982,42.0752144,42.6123962,43.6161919,42.1932831,
      43.1055450,44.3518715,45.3961258,45.0790215,41.8874397,40.2159805,
      40.2447319,39.6208458,38.6873589,37.9272423,36.8718872,36.8310852,
      37.4524879,37.3440933,37.9861374,40.3810501,41.3464622,42.6495285,
      42.6096764,40.3134537,39.7971268,41.5401535,40.7160759,41.0363541,
      41.8171883,42.4190292,43.0318832,43.9968109,44.0419617,44.3225212,
      44.6082611,43.2199631,42.0419197,41.9679718,42.4926224,43.2091255,
      43.2512283,41.2301674,40.1057358,40.4510574,41.5329170,41.5678177,
      43.0090141,42.1592140,39.9234505,38.8394127,40.4319878,40.8679352,
      41.4551926,41.9756317,43.9878922,46.5736389,45.5939293,42.4487762,
      41.5325394,42.8830910,44.5771217,45.8541985,46.8249474,47.5686378,
      46.6700745,45.4120026,43.2305107,42.7635345,43.7112923,42.0768661,
      41.1835632,40.3352280,37.9761467,35.9550056,36.3212509,36.9925880,
      37.2625008,37.0040665,38.5232544,39.4119797,41.8316803,43.7091446,
      42.9381447,42.1066780,40.3771248,38.6518707,37.0550499,36.9447708,
      38.1017685,39.4727097,39.8670387,39.3820763,38.2180786,37.7543488,
      37.7265244,38.0290642,37.5531158,37.4685936,39.8233147,42.0480766,
      42.4053535,43.0117416,44.1289330,45.0393829,45.1114540,45.0086479,
      44.6560631,45.0278931,46.7830849,48.7649765,47.7991905,46.5339661,
      43.3679199,41.6420822,41.2694893,41.5959740,43.5330009,43.3643608,
      42.1471291,42.5552788,42.4521446,41.7629128,39.9476891,38.3217010,
      40.5318718,42.8811569,44.4796944,44.6887932,43.1670265,41.2226143,
      41.8330154,44.3721924,45.2697029,44.4174194,43.5068550,44.9793015,
      45.0585403,43.2746620,40.3317070,40.3880501,40.2627106,39.6230278,
      41.0305252,40.9262009,40.8326912,41.7084885,42.9038048,45.8650513,
      46.5231590,47.9916115,47.8463135,46.5921936,45.8854408,45.9130440,
      45.7450371,46.2964249,44.9394569,45.8141251,47.5284042,48.5527802,
      48.3950577,47.8753052,45.8880005,45.7086983,44.6174774,43.5567932,
      44.5891113,43.1778679,40.9405632,40.6206894,41.3330421,42.2759552,
      42.4744949,43.0719833,44.2178459,43.8956337,44.1033440,45.6241455,
      45.3724861,44.9167595,45.9180603,46.9077835,46.1666603,46.6013489,
      46.6592331,46.7291603,47.1908340,45.9784355,45.1215782,45.6791115,
      46.7379875,47.3036957,45.9968834,44.4669495,45.7734680,44.6315041,
      42.9911766,46.3842583,43.7214432,43.5276833,41.3946495,39.7013168,
      39.1033401,38.5292892,41.0096245,43.4535828,44.6525154,45.5725899,
      46.2815285,45.2766647,45.3481712,45.5039482,45.6745682,44.0144806,
      42.9305000,43.6785469,42.2500534,40.0007210,40.4477005,41.4432716,
      42.0058670,42.9357758,45.6758842,46.8809929,46.8601494,47.0449791,
      46.5420647,46.8939934,46.2963371,43.5479164,41.3864059,41.4046364,
      42.3037987,43.6223717,45.8602371,47.3016396,46.8632469,45.4651413,
      45.6275482,44.9968376,42.7558670,42.0218239,41.9883728,42.2571678,
      44.3708687,45.7483635,44.8832512,44.7945862,44.8922577,44.7409401,
      45.1726494,45.5686874,45.9946709,47.3151054,48.0654068,46.4817467,
      42.8618279,42.4550323,42.5791168,43.4230957,44.7787971,43.8317108,
      43.6481781,42.4183960,41.8426285,43.3475227,44.4749908,46.3498306,
      47.8599319,46.2449913,43.6044006,42.4563484,41.2715340,39.8492508,
      39.9997292,41.4410820,42.9388237,42.5687332};
	
    // Actual values of the outlier contaminated series for
	// t = 181,...,190
    double[] exactForecastOutlierContaminatedSeries = {
	  42.6384087,41.7088661,43.9399033,45.4284401,44.4558411,45.1761856,
	  45.3489113,45.1892662, 46.3754730,45.6082802 };

    // Actual values of the outlier free series for
	// t = 181,...,190. This are the values of the outlier contaminated
    // series - 2.5
    double[] exactForecastOutlierFreeSeries = {
	  40.1384087,39.2088661,41.4399033,42.9284401,41.9558411,42.6761856,
	  42.8489113,42.6892662, 43.8754730,43.1082802 };

      model[0] = 2;
      model[1] = 1;
      model[2] = 1;
      model[3] = 0;

	  ARMAOutlierIdentification armaOutlier =
				new ARMAOutlierIdentification(outlierContaminatedSeries);

	  armaOutlier.setRelativeError(1.0e-5);
      armaOutlier.compute(model);
	  armaOutlier.computeForecasts(nForecast);

      numOutliers = armaOutlier.getNumberOfOutliers();
      outlierStatistics = armaOutlier.getOutlierStatistics();
      constant = armaOutlier.getConstant();
      ar = armaOutlier.getAR();
	  ma = armaOutlier.getMA();
      resStdErr = armaOutlier.getResidualStandardError();
      aic = armaOutlier.getAIC();

	  outlierContaminatedForecast = armaOutlier.getForecast();
	  outlierFreeForecast = armaOutlier.getOutlierFreeForecast();
	  probabilityLimits = armaOutlier.getDeviations();
	  psiWeights = armaOutlier.getPsiWeights();

	  System.out.printf("%n%n   ARMA parameters:%n");
	  System.out.printf(Locale.ENGLISH, "constant: %9.6f%n", constant);
	  System.out.printf(Locale.ENGLISH, "ar[0]: %12.6f%n", ar[0]);
	  System.out.printf(Locale.ENGLISH, "ar[1]: %12.6f%n", ar[1]);
	  System.out.printf(Locale.ENGLISH, "ma[0]: %12.6f%n%n", ma[0]);
	  System.out.printf("Number of outliers: %d%n%n", numOutliers);
	  System.out.printf("   Outlier statistics:%n");
	  System.out.printf("Time point%8sOutlier type%n", " ");
	  for (int i = 0; i < numOutliers; i++)
		  System.out.printf("%10d%20d%n", outlierStatistics[i][0],
							  outlierStatistics[i][1]);
	  System.out.printf(Locale.ENGLISH, "%nRSE:%9.6f%n", resStdErr);
	  System.out.printf(Locale.ENGLISH, "AIC:%12.6f%n%n", aic);
	  System.out.printf("%5s* * * Forecast Table for outlier "
		                 + "contaminated series * * *%n%n", " ");
	  System.out.printf("%10sExact%5sforecast%6slimits%9spsi%n",
		                " ", " ", " ", " ");
	  for (int i = 0; i < nForecast; i++)
		  System.out.printf(Locale.ENGLISH, "%7s%8.4f%13.4f%12.4f%12.4f%n",
			  " ",
			  exactForecastOutlierContaminatedSeries[i],
			  outlierContaminatedForecast[i],
		      probabilityLimits[i],
			  psiWeights[i]);

	  System.out.printf("%n%n%5s* * * Forecast Table for outlier "+
		                "free series * * *%n%n", " ");
	  System.out.printf("%10sExact%5sforecast%6slimits%9spsi%n",
		                " ", " ", " ", " ");
	  for (int i = 0; i < nForecast; i++)
		  System.out.printf(Locale.ENGLISH, "%7s%8.4f%13.4f%12.4f%12.4f%n",
			    " ",
			    exactForecastOutlierFreeSeries[i],
				outlierFreeForecast[i],
		        probabilityLimits[i],
				psiWeights[i]);
    }
}

Output



   ARMA parameters:
constant:  8.891421
ar[0]:     0.944052
ar[1]:    -0.150404
ma[0]:    -0.558928

Number of outliers: 2

   Outlier statistics:
Time point        Outlier type
       150                   2
       200                   1

RSE: 1.004306
AIC: 1323.617443

     * * * Forecast Table for outlier contaminated series * * *

          Exact     forecast      limits         psi
        42.6384      42.3158      1.9684      1.5030
        41.7089      42.7933      3.5535      1.2685
        43.9399      43.2822      4.3430      0.9715
        45.4284      43.6718      4.7453      0.7263
        44.4558      43.9662      4.9560      0.5396
        45.1762      44.1854      5.0686      0.4002
        45.3489      44.3482      5.1294      0.2966
        45.1893      44.4688      5.1625      0.2198
        46.3755      44.5582      5.1806      0.1629
        45.6083      44.6245      5.1906      0.1207


     * * * Forecast Table for outlier free series * * *

          Exact     forecast      limits         psi
        40.1384      40.5904      1.9684      1.5030
        39.2089      41.0679      3.5535      1.2685
        41.4399      41.5567      4.3430      0.9715
        42.9284      41.9464      4.7453      0.7263
        41.9558      42.2407      4.9560      0.5396
        42.6762      42.4600      5.0686      0.4002
        42.8489      42.6227      5.1294      0.2966
        42.6893      42.7434      5.1625      0.2198
        43.8755      42.8328      5.1806      0.1629
        43.1083      42.8991      5.1906      0.1207
Link to Java source.