com.imsl.stat
Class AutoARIMA

java.lang.Object
  com.imsl.stat.AutoARIMA

All Implemented Interfaces:

Serializable, Cloneable

public class AutoARIMA
extends Object
implements Serializable, Cloneable

Automatically identifies time series outliers, determines parameters of a multiplicative seasonal model and produces forecasts that incorporate the effects of outliers whose effects persist beyond the end of the series.

Class AutoARIMA determines the parameters of a multiplicative seasonal model, and then uses the fitted model to identify outliers and prepare forecasts. The order of this model can be specified or automatically determined through use of an overloaded compute method. Potential missing values in the time series are estimated prior to the parameter and outlier computations.

The model handled by class AutoARIMA has the following form:

where

and

It is assumed that all roots of and lie outside the unit circle. Clearly, if the model reduces to the traditional model.

is the unobserved, outlier-free time series with mean , and white noise . This model is referred to as the underlying, outlier-free model. Class AutoARIMA does not assume that this series is observable. It assumes that the observed values might be contaminated by one or more outliers, whose effects are added to the underlying outlier-free series:

Outlier identification uses the algorithm developed by Chen and Liu (1993). Outliers are classified into 1 of 5 types:

1. innovational
2. additive
3. level shift
4. temporary change and
5. unable to identify

Once the model parameters are estimated and the outliers are identified, class AutoARIMA estimates , the outlier-free series representation of the data, by removing the estimated outlier effects. Parameter estimation and outlier detection are based on methods from class ARMAOutlierIdentification.

Using the information about the adjusted model and the removed outliers, forecasts are then prepared for the outlier-free series. Outlier effects are added to these forecasts to produce a forecast for the observed series, . If there are no outliers, then the forecasts for the outlier-free series and the observed series will be identical.

Model selection techniques

Users have an option of either specifying specific values for or have class AutoARIMA automatically select best fit values. Model selection can be conducted in one of three ways listed below depending upon which compute method of class AutoARIMA is invoked.

Technique 1: Automatic Selection

This technique, chosen by use of method compute(int maxlag), tries to fit a model of the form

to the outlier free series .

It initially searches for the representation with minimum value of the chosen information criterion (AIC, AICC or BIC) for the noisy data, where .

If the user calls methods setPeriods and setDifferenceOrders prior to invoking the compute method, then the values in arrays periods and orders are included in the search to find an optimum representation of the series. Here, every possible combination of values for s, d in periods and orders, respectively, are examined. The best found model order is then used as input for the parameter and outlier detection routine.

The optimum values for p, q, s and d are returned through method getOptimumModelOrder.

Technique 2: Grid Search

This technique, chosen by means of method compute(int[] arOrders, int[] maOrders), conducts a grid search for p and q using all possible combinations of candidate values in arOrders and maOrders.

If methods setPeriods and setDifferenceOrders are called prior to invoking the compute method, then the grid search is extended to include the candidate values for s and d given in arrays periods and orders, respectively.

If method setDifferenceOrders is not called prior to compute, then by default, and therefore no seasonal adjustment is attempted. The grid search is then restricted to searching for optimum values of p and q only.

The optimum values for p, q, s and d are contained in the array returned by method getOptimumModelOrder.

Technique 3: Specified Model

In the third technique, selectable by means of method compute(int p, int q, int s, int d), specific values for p, q, s and d are given. This technique has essentially the same functionality as class ARMAOutlierIdentification but with the additional option of missing value estimation.

Outliers

The algorithm of Chen and Liu (1993) is used to identify outliers. The number of outliers identified is returned via method getNumberOfOutliers. Both the time and classification for these outliers are contained in the matrix returned by method getOutlierStatistics. Outliers are classified into one of five categories based upon the standardized statistic for each outlier type. The time at which the outlier occurred is given in the first column of the returned matrix. The outlier identifier returned in the second column is according to the descriptions in the following table:

Outlier Identifier	Name	General Description
INNOVATIONAL	Innovational Outlier (IO)	Innovational outliers persist. That is, there is an initial impact at the time the outlier occurs. This effect continues in a lagged fashion with all future observations. The lag coefficients are determined by the coefficients of the underlying model.
ADDITIVE	Additive Outlier (AO)	Additive outliers do not persist. As the name implies, an additive outlier affects only the observation at the time the outlier occurs. Hence additive outliers have no effect on future forecasts.
LEVEL_SHIFT	Level Shift (LS)	Level shift outliers persist. They have the effect of either raising or lowering the mean of the series starting at the time the outlier occurs. This shift in the mean is abrupt and permanent.
TEMPORARY_CHANGE	Temporary Change (TC)	Temporary change outliers persist and are similar to level shift outliers with one major exception. Like level shift outliers, there is an abrupt change in the mean of the series at the time this outlier occurs. However, unlike level shift outliers, this shift is not permanent. The TC outlier gradually decays, eventually bringing the mean of the series back to its original value. The rate of this decay is modeled using method setDelta. The default of delta = 0.7 is the value recommended for general use by Chen and Liu (1993).
UNABLE_TO_IDENTIFY	Unable to Identify (UI)	If an outlier is identified as the last observation, then the algorithm is unable to determine the outlier's classification. For forecasting, a UI outlier is treated as an IO outlier. That is, its effect is lagged into the forecasts.

Except for additive outliers (AO), the effect of an outlier persists to observations following that outlier. Forecasts produced by methods of class AutoARIMA take this into account.

For more information on forecasting an outlier contaminated series, see the description of class ARMAOutlierIdentification.

See Also:

Example 1, Example 2, Example 3, Serialized Form

Nested Class Summary

static class

AutoARIMA.NoAcceptableModelFoundException No appropriate ARIMA model could be found.

Field Summary

static int	ADDITIVE Indicates detection of an additive outlier.
static int	AIC Indicates that Akaike's information criterion (AIC) is used in the optimum model determination.
static int	AICC Indicates that Akaike's corrected information criterion (AICC) is used in the optimum model determination.
static int	BIC Indicates that the Bayesian information criterion (BIC) is used in the optimum model determination.
static int	INNOVATIONAL Indicates detection of an innovational outlier.
static int	LEVEL_SHIFT Indicates detection of a level shift outlier.
static int	TEMPORARY_CHANGE Indicates detection of a temporary change outlier.
static int	UNABLE_TO_IDENTIFY Indicates detection of an outlier that cannnot be categorized.

Constructor Summary

AutoARIMA(int[] times, double[] x)
Constructor for AutoARIMA.

Method Summary

void	compute(int maxlag) Estimates potential missing values, detects and determines outliers and simultaneously fits an optimum model from a set of different models to the outlier free time series.
void	compute(int[] arOrders, int[] maOrders) Estimates potential missing values, detects and determines outliers and simultaneously fits an optimum model from a set of different models to the outlier free time series.
void	compute(int p, int q, int s, int d) Estimates potential missing values, detects and determines outliers and simultaneously fits an model to the outlier free time series.
void	forecast(int nForecast) Computes forecasts, associated probability limits and weights for the given outlier contaminated time series.
double	getAIC() Returns Akaike's information criterion (AIC) for the optimum model.
double	getAICC() Returns Akaike's Corrected Information Criterion (AICC) for the optimum model.
double[]	getAR() Returns the final autoregressive parameter estimates of the optimum model.
double	getBIC() Returns the Bayesian Information Criterion (BIC) for the optimum model.
int[]	getCompleteTimes() Returns all time points at which the original series was observed, including values for times with missing values in x.
double[]	getCompleteTimeSeries() Returns the original series with potentially missing values replaced by estimates.
double	getConstant() Returns the constant parameter estimate for the optimum model.
double[]	getDeviations() Returns the deviations used for calculating the forecast confidence limits.
double[]	getForecast() Returns forecasts for the original outlier contaminated series.
double[]	getMA() Returns the final moving average parameter estimates of the optimum model.
int	getNumberOfOutliers() Returns the number of outliers detected.
int[]	getOptimumModelOrder() Returns the order of the optimum model.
double[]	getOutlierFreeForecast() Returns forecasts for the outlier free series.
double[]	getOutlierFreeSeries() Returns the outlier free series.
int[][]	getOutlierStatistics() Returns the outlier statistics.
double[]	getPsiWeights() Returns the weights of the infinite order moving average form of the model.
double[]	getResiduals() Returns the residuals.
double	getResidualStandardError() Returns the residual standard error of the outlier free series.
void	setAccuracyTolerance(double epsilon) Sets the tolerance value controlling the accuracy of the parameter estimates.
void	setConfidence(double confidence) Sets the confidence level for calculating confidence limit deviations returned by getDeviations.
void	setCriticalValue(double critical) Sets the critical value used as a threshold during outlier detection.
void	setDelta(double delta) Sets the dampening effect parameter.
void	setDifferenceOrders(int[] orders) Defines the orders of the periodic differences used in the determination of the optimum model.
void	setMaximumARLag(int maxARLag) Defines the maximum AR lag used in the determination of the optimum (s,d) combination of method compute(int[] arOrders, int[] maOrders).
void	setModelSelectionCriterion(int infoCriterion) Sets the model selection criterion.
void	setPeriods(int[] periods) Defines the periods used in the determination of the optimum model.
void	setRelativeError(double relativeError) Sets the stopping criterion for use in the nonlinear equation solver.

Methods inherited from class java.lang.Object

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

Field Detail

ADDITIVE

public static final int ADDITIVE

Indicates detection of an additive outlier.

See Also:

Constant Field Values

AIC

public static final int AIC

Indicates that Akaike's information criterion (AIC) is used in the optimum model determination.

See Also:

Constant Field Values

AICC

public static final int AICC

Indicates that Akaike's corrected information criterion (AICC) is used in the optimum model determination.

See Also:

Constant Field Values

BIC

public static final int BIC

Indicates that the Bayesian information criterion (BIC) is used in the optimum model determination.

See Also:

Constant Field Values

INNOVATIONAL

public static final int INNOVATIONAL

Indicates detection of an innovational outlier.

See Also:

Constant Field Values

LEVEL_SHIFT

public static final int LEVEL_SHIFT

Indicates detection of a level shift outlier.

See Also:

Constant Field Values

TEMPORARY_CHANGE

public static final int TEMPORARY_CHANGE

Indicates detection of a temporary change outlier.

See Also:

Constant Field Values

UNABLE_TO_IDENTIFY

public static final int UNABLE_TO_IDENTIFY

Indicates detection of an outlier that cannnot be categorized.

See Also:

Constant Field Values

Constructor Detail

AutoARIMA

public AutoARIMA(int[] times,
                 double[] x)

Constructor for AutoARIMA.

Parameters:

times - an int array of length nObs, where nObs is the number of observed time series values, containing the time points at which the time series was observed. It is required that are in strictly increasing order. Times for missing values are identified as non-incremental gaps in this series. A gap of missing values in x is assumed if the difference between two consecutive values is greater than 1, i.e. . The difference is the number of missing values in the gap.

x - a double array containing the observations at the times given in array times. This series can contain outliers and missing observations.

Method Detail

compute

public final void compute(int maxlag)
                   throws ARMA.MatrixSingularException,
                          ARMA.TooManyCallsException,
                          ARMA.IncreaseErrRelException,
                          ARMA.NewInitialGuessException,
                          ARMA.IllConditionedException,
                          ARMA.TooManyITNException,
                          ARMA.TooManyFcnEvalException,
                          ARMA.TooManyJacobianEvalException,
                          ARAutoUnivariate.TriangularMatrixSingularException,
                          ARMAMaxLikelihood.NonInvertibleException,
                          ARMAMaxLikelihood.NonStationaryException,
                          ARMAMaxLikelihood.InitialMAException,
                          ZeroPolynomial.DidNotConvergeException,
                          SingularMatrixException,
                          Cholesky.NotSPDException,
                          AutoARIMA.NoAcceptableModelFoundException

Estimates potential missing values, detects and determines outliers and simultaneously fits an optimum model from a set of different models to the outlier free time series.

Parameters:

maxlag - the maximum value for p allowed when fitting models to the given series, . It is required that x.length. The optimum model is determined according to the model selection criterion chosen by the user, see method setModelSelectionCriterion.

Throws:

ARMA.MatrixSingularException - is thrown if the input matrix is singular.

ARMA.TooManyCallsException - is thrown if the number of calls to the function has exceeded the maximum number of iterations times the number of moving average (MA) parameters + 1.

ARMA.IncreaseErrRelException - is thrown if the bound for the relative error is too small.

ARMA.NewInitialGuessException - is thrown if the iteration has not made good progress.

ARMA.IllConditionedException - is thrown if the problem is ill-conditioned.

ARMA.TooManyITNException - is thrown if the maximum number of iterations is exceeded.

ARMA.TooManyFcnEvalException - is thrown if the maximum number of function evaluations is exceeded.

ARMA.TooManyJacobianEvalException - is thrown if the maximum number of Jacobian evaluations is exceeded.

ARAutoUnivariate.TriangularMatrixSingularException - is thrown if the input matrix to ARAutoUnivariate is singular.

ARMAMaxLikelihood.NonInvertibleException - is thrown if the intermediate or final maximum likelihood estimates for the time series are noninvertible.

ARMAMaxLikelihood.NonStationaryException - is thrown if the intermediate or final maximum likelihood estimates for the time series are nonstationary.

ARMAMaxLikelihood.InitialMAException - is thrown if the initial values provided for the moving average terms are noninvertible. In this case, ARMAMaxLikelihood terminates and does not compute the time series estimates.

ZeroPolynomial.DidNotConvergeException - is thrown if the algorithm computing the roots of the AR- or MA- polynomial does not converge.

SingularMatrixException - is thrown if during the computation of a small perturbation of the matrix product , it is found that A, the matrix used in the determination of the weights, is singular.

Cholesky.NotSPDException - is thrown if during the computation of a small perturbation of the matrix product , it is found that A, the matrix used in the determination of the weights, is not positive definite.

AutoARIMA.NoAcceptableModelFoundException - is thrown if no appropriate ARIMA model for the given time series could be found.

compute

public final void compute(int[] arOrders,
                          int[] maOrders)
                   throws ARMA.MatrixSingularException,
                          ARMA.TooManyCallsException,
                          ARMA.IncreaseErrRelException,
                          ARMA.NewInitialGuessException,
                          ARMA.IllConditionedException,
                          ARMA.TooManyITNException,
                          ARMA.TooManyFcnEvalException,
                          ARMA.TooManyJacobianEvalException,
                          ARAutoUnivariate.TriangularMatrixSingularException,
                          ARMAMaxLikelihood.NonInvertibleException,
                          ARMAMaxLikelihood.NonStationaryException,
                          ARMAMaxLikelihood.InitialMAException,
                          ZeroPolynomial.DidNotConvergeException,
                          SingularMatrixException,
                          Cholesky.NotSPDException,
                          AutoARIMA.NoAcceptableModelFoundException

Estimates potential missing values, detects and determines outliers and simultaneously fits an optimum model from a set of different models to the outlier free time series.

Parameters:

arOrders - an int array containing all possible AR orders to consider in the optimum model search. It is required that all values in arOrders are greater than or equal to zero.

maOrders - an int array containing all possible MA orders to consider in the optimum model search. It is required that all values in maOrders are greater than or equal to zero.

Throws:

ARMA.MatrixSingularException - is thrown if the input matrix is singular.

ARMA.TooManyCallsException - is thrown if the number of calls to the function has exceeded the maximum number of iterations times the number of moving average (MA) parameters + 1.

ARMA.IncreaseErrRelException - is thrown if the bound for the relative error is too small.

ARMA.NewInitialGuessException - is thrown if the iteration has not made good progress.

ARMA.IllConditionedException - is thrown if the problem is ill-conditioned.

ARMA.TooManyITNException - is thrown if the maximum number of iterations is exceeded.

ARMA.TooManyFcnEvalException - is thrown if the maximum number of function evaluations is exceeded.

ARMA.TooManyJacobianEvalException - is thrown if the maximum number of Jacobian evaluations is exceeded.

ARAutoUnivariate.TriangularMatrixSingularException - is thrown if the input matrix to ARAutoUnivariate is singular.

ARMAMaxLikelihood.NonInvertibleException - is thrown if the intermediate or final maximum likelihood estimates for the time series are noninvertible.

ARMAMaxLikelihood.NonStationaryException - is thrown if the intermediate or final maximum likelihood estimates for the time series are nonstationary.

ARMAMaxLikelihood.InitialMAException - is thrown if the initial values provided for the moving average terms are noninvertible. In this case, ARMAMaxLikelihood terminates and does not compute the time series estimates.

ZeroPolynomial.DidNotConvergeException - is thrown if the algorithm computing the roots of the AR- or MA- polynomial does not converge.

SingularMatrixException - is thrown if during the computation of a small perturbation of the matrix product , it is found that A, the matrix used in the determination of the weights, is singular.

Cholesky.NotSPDException - is thrown if during the computation of a small perturbation of the matrix product , it is found that A, the matrix used in the determination of the weights, is not positive definite.

AutoARIMA.NoAcceptableModelFoundException - is thrown if no appropriate ARIMA model for the given time series could be found.

compute

public final void compute(int p,
                          int q,
                          int s,
                          int d)
                   throws ARMA.MatrixSingularException,
                          ARMA.TooManyCallsException,
                          ARMA.IncreaseErrRelException,
                          ARMA.NewInitialGuessException,
                          ARMA.IllConditionedException,
                          ARMA.TooManyITNException,
                          ARMA.TooManyFcnEvalException,
                          ARMA.TooManyJacobianEvalException,
                          ARAutoUnivariate.TriangularMatrixSingularException,
                          ARMAMaxLikelihood.NonInvertibleException,
                          ARMAMaxLikelihood.NonStationaryException,
                          ARMAMaxLikelihood.InitialMAException,
                          ZeroPolynomial.DidNotConvergeException,
                          SingularMatrixException,
                          Cholesky.NotSPDException,
                          AutoARIMA.NoAcceptableModelFoundException

Estimates potential missing values, detects and determines outliers and simultaneously fits an model to the outlier free time series.

Parameters:

p - a non-negative scalar int, the order of the AR part of the model.

q - a non-negative scalar int, the order of the MA part of the model.

s - a positive scalar int, the period of the difference used in the model.

d - a non-negative scalar int, the order of the difference used in the model.

Throws:

ARMA.MatrixSingularException - is thrown if the input matrix is singular.

ARMA.TooManyCallsException - is thrown if the number of calls to the function has exceeded the maximum number of iterations times the number of moving average (MA) parameters + 1.

ARMA.IncreaseErrRelException - is thrown if the bound for the relative error is too small.

ARMA.NewInitialGuessException - is thrown if the iteration has not made good progress.

ARMA.IllConditionedException - is thrown if the problem is ill-conditioned.

ARMA.TooManyITNException - is thrown if the maximum number of iterations is exceeded.

ARMA.TooManyFcnEvalException - is thrown if the maximum number of function evaluations is exceeded.

ARMA.TooManyJacobianEvalException - is thrown if the maximum number of Jacobian evaluations is exceeded.

ARAutoUnivariate.TriangularMatrixSingularException - is thrown if the input matrix to ARAutoUnivariate is singular.

ARMAMaxLikelihood.NonInvertibleException - is thrown if the intermediate or final maximum likelihood estimates for the time series are noninvertible.

ARMAMaxLikelihood.NonStationaryException - is thrown if the intermediate or final maximum likelihood estimates for the time series are nonstationary.

ARMAMaxLikelihood.InitialMAException - is thrown if the initial values provided for the moving average terms are noninvertible. In this case, ARMAMaxLikelihood terminates and does not compute the time series estimates.

ZeroPolynomial.DidNotConvergeException - is thrown if the algorithm computing the roots of the AR- or MA- polynomial does not converge.

SingularMatrixException - is thrown if during the computation of a small perturbation of the matrix product , it is found that A, the matrix used in the determination of the weights, is singular.

Cholesky.NotSPDException - is thrown if during the computation of a small perturbation of the matrix product , it is found that A, the matrix used in the determination of the weights, is not positive definite.

AutoARIMA.NoAcceptableModelFoundException - is thrown if no appropriate ARIMA model for the given time series could be found.

forecast

public final void forecast(int nForecast)

Computes forecasts, associated probability limits and weights for the given outlier contaminated time series. Note that one of the compute methods must be invoked first before invoking this method. Otherwise, the method throws an IllegalStateException exception.

Parameters:

nForecast - an int scalar representing the number of forecasts that will be computed. nForecast must be greater than 0. Forecast origin is the time point of the last observed value in the time series, . Forecasts are computed for lead times , i.e. time points .

getAIC

public double getAIC()

Returns Akaike's information criterion (AIC) for the optimum model.

Returns:

a double scalar containing Akaike's information criterion (AIC) for the optimum outlier free series. One of the compute methods must be called before invoking this method. Otherwise, an IllegalStateException exception is thrown.

getAICC

public double getAICC()

Returns Akaike's Corrected Information Criterion (AICC) for the optimum model.

Returns:

a double scalar containing Akaike's Corrected Information Criterion (AICC) for the optimum outlier free series. One of the compute methods must be called before invoking this method. Otherwise, an IllegalStateException exception is thrown.

getAR

public double[] getAR()

Returns the final autoregressive parameter estimates of the optimum model. Note that one of the compute methods must be invoked first before invoking this method. Otherwise, the method throws an IllegalStateException exception.

Returns:

a double array containing the final autoregressive parameter estimates. If the optimum model has no AR component then a zero-length array is returned.

getBIC

public double getBIC()

Returns the Bayesian Information Criterion (BIC) for the optimum model.

Returns:

a double scalar containing the Bayesian Information Criterion (BIC) for the optimum outlier free series. One of the compute methods must be called before invoking this method. Otherwise, an IllegalStateException exception is thrown.

getCompleteTimes

public int[] getCompleteTimes()

Returns all time points at which the original series was observed, including values for times with missing values in x.

Returns:

an int array of length times[times.length-1]-times[0]+1 containing the times at which the time series (including missing values) was observed. One of the compute methods must be called before invoking this method. Otherwise, an IllegalStateException exception is thrown.

getCompleteTimeSeries

public double[] getCompleteTimeSeries()

Returns the original series with potentially missing values replaced by estimates.

Returns:

a double array containing the original time series with missing values replaced by estimates. One of the compute methods must be called before invoking this method. Otherwise, an IllegalStateException exception is thrown.

getConstant

public double getConstant()

Returns the constant parameter estimate for the optimum model. Note that one of the compute methods must be invoked first before invoking this method. Otherwise, the method throws an IllegalStateException exception.

Returns:

a double scalar containing the constant parameter estimate for the optimum model.

getDeviations

public double[] getDeviations()

Returns the deviations used for calculating the forecast confidence limits.

Returns:

a double array of length nForecast containing the deviations from each forecast for calculating forecast confidence intervals. The confidence level is specified in confidence. Method forecast must be invoked before this method is called. Otherwise, an IllegalStateException exception is thrown. By default, confidence = 0.95.

getForecast

public double[] getForecast()

Returns forecasts for the original outlier contaminated series.

Returns:

a double array of length nForecast containing the forecasts for the original series. Forecast origin is the time point of the last observed value in the time series, . Forecasts are returned for lead times , i.e. time points . Method forecast must be invoked before this method is called. Otherwise, an IllegalStateException exception is thrown.

getMA

public double[] getMA()

Returns the final moving average parameter estimates of the optimum model. Note that one of the compute methods must be invoked first before invoking this method. Otherwise, the method throws an IllegalStateException exception.

Returns:

a double array containing the final moving average parameter estimates. If the optimum model has no MA component then a zero-length array is returned.

getNumberOfOutliers

public int getNumberOfOutliers()

Returns the number of outliers detected.

Returns:

an int scalar containing the number of outliers detected. The compute method must be invoked first before invoking this method. Otherwise, the method throws an IllegalStateException exception.

getOptimumModelOrder

public int[] getOptimumModelOrder()

Returns the order of the optimum model. Note that one of the compute methods must be invoked first before calling this method. Otherwise, an IllegalStateException exception is thrown.

Returns:

an int array of length 4 containing the values p, q, s and d for the optimum model.

getOutlierFreeForecast

public double[] getOutlierFreeForecast()

Returns forecasts for the outlier free series.

Returns:

a double array of length nForecast containing the forecasts for the outlier free series. Forecast origin is the time point of the last observed value in the time series, . Forecasts are returned for lead times , i.e. time points . Method forecast must be invoked before this method is called. Otherwise, an IllegalStateException exception is thrown.

getOutlierFreeSeries

public double[] getOutlierFreeSeries()

Returns the outlier free series.

Returns:

a double array containing the original time series with estimated missing values after removal of any outlier effects. One of the compute methods must be called before invoking this method. Otherwise, an IllegalStateException exception is thrown.

getOutlierStatistics

public int[][] getOutlierStatistics()

Returns the outlier statistics.

Returns:

a double array of length nOutliers by 2, where nOutliers is the number of detected outliers, containing the outlier statistics. The first column contains the time at which the outlier was observed (time ranging from times[0] to times[times.length - 1]) and the second column contains an identifier indicating the type of outlier observed. Outlier types fall into one of five categories:

Identifier	Outlier type
INNOVATIONAL	Innovational Outliers (IO)
ADDITIVE	Additive Outliers (AO)
LEVEL_SHIFT	Level Shift Outliers (LS)
TEMPORARY_CHANGE	Temporary Change Outliers (TC)
UNABLE_TO_IDENTIFY	Unable to Identify (UI)

If the number of detected outliers equals zero, then an array of length zero is returned.

getPsiWeights

public double[] getPsiWeights()

Returns the weights of the infinite order moving average form of the model.

Returns:

a double array of length nForecast containing the weights of the infinite order moving average form of the optimum model for the outlier free series. Method forecast must be invoked before this method is called. Otherwise, an IllegalStateException exception is thrown.

getResiduals

public double[] getResiduals()

Returns the residuals. Note that one of the compute methods must be invoked first before invoking this method. Otherwise, getResiduals throws an IllegalStateException exception.

Returns:

a double array containing the residuals for the outlier and gap free series at the final parameter estimation point.

getResidualStandardError

public double getResidualStandardError()

Returns the residual standard error of the outlier free series. Note that one of the compute methods must be invoked first before calling this method. Otherwise, an IllegalStateException exception is thrown.

Returns:

a double scalar containing the standard error of the outlier free series.

setAccuracyTolerance

public void setAccuracyTolerance(double epsilon)

Sets the tolerance value controlling the accuracy of the parameter estimates.

Parameters:

epsilon - a double scalar, a positive tolerance value controlling the accuracy of parameter estimates during outlier detection. Default: epsilon = 0.001.

setConfidence

public void setConfidence(double confidence)

Sets the confidence level for calculating confidence limit deviations returned by getDeviations.

Parameters:

confidence - a double scalar specifying the confidence level used in computing forecast confidence intervals. Typical choices for confidence are 0.90, 0.95, and 0.99. confidence must be greater than 0.0 and less than 1.0. Default: confidence = 0.95.

setCriticalValue

public void setCriticalValue(double critical)

Sets the critical value used as a threshold during outlier detection.

Parameters:

critical - a double scalar, the critical value used as a threshold for the statistics used in the outlier detection. critical must be greater than zero. Default: critical = 3.0.

setDelta

public void setDelta(double delta)

Sets the dampening effect parameter.

Parameters:

delta - a double scalar, the dampening effect parameter used in the detection of a Temporary Change Outlier (TC). delta must be greater than 0 and less than 1. Default: delta = 0.7.

setDifferenceOrders

public void setDifferenceOrders(int[] orders)

Defines the orders of the periodic differences used in the determination of the optimum model.

Parameters:

orders - an int array containing all possible orders for each difference given in periods. All elements in orders must be non-negative. By default, orders is a one-element array with orders[0] = 0.

setMaximumARLag

public void setMaximumARLag(int maxARLag)

Defines the maximum AR lag used in the determination of the optimum (s,d) combination of method compute(int[] arOrders, int[] maOrders).

Parameters:

maxARLag - a scalar int, the maximum AR lag used in the computation of the optimum (s,d) combination for method compute(int[] arOrders, int[] maOrders). It is required that maxARLag is greater than zero and smaller than the original series after replacement of potential missing values. By default, maxARLag = 10.

setModelSelectionCriterion

public void setModelSelectionCriterion(int infoCriterion)

Sets the model selection criterion.

Parameters:

infoCriterion - an int scalar indicating the model selection criterion used in the search for the optimum model.

infoCriterion	Model selection criterion
AIC	Akaike's information criterion (AIC)
AICC	Akaike's corrected information criterion (AICC)
BIC	Bayesian information criterion (BIC)

By default, AIC is chosen.

setPeriods

public void setPeriods(int[] periods)

Defines the periods used in the determination of the optimum model.

Parameters:

periods - an int array containing all possible periods that can be applied to the original series after insertion of missing values. All elements of periods must be positive. By default, periods is a one-element array with periods[0] = 1;

setRelativeError

public void setRelativeError(double relativeError)

Sets the stopping criterion for use in the nonlinear equation solver.

Parameters:

relativeError - a double positive scalar containing the stopping criterion for use in the nonlinear equation solver used in the least-squares algorithm. Default: relativeError = 2.2204460492503131e-012.