Class ARMA
 All Implemented Interfaces:
Serializable
Given a time series of data, the ARMA model is a tool for understanding and, perhaps, predicting future values in this series. The AR part involves regressing the variable on its own lagged values. The MA part involves modeling the error term as a linear combination of error terms occurring contemporaneously and at various times in the past. The model is usually referred to as the ARMA(p,q) model where p is the order of the AR part and q is the order of the MA part.
 See Also:

Constructor Summary
ConstructorDescriptionARMA
(double[] x, double[] ar, double[] ma, double b, double[] fittedValues, double[] residuals) Constructor. 
Method Summary
Modifier and TypeMethodDescriptiondouble
Returns adjusted R^{2} statistic.double[]
ar()
Returns the linear coefficients of AR(p).int
df()
Returns the degreeoffreedom of residual standard error.static ARMA
fit
(double[] x, int p, int q) Fits an ARMA model with HannanRissanen algorithm.double[]
Returns the fitted values.double
forecast()
Returns 1step ahead forecast.double[]
forecast
(int l) Returns lstep ahead forecast.double
Returns the intercept.double[]
ma()
Returns the linear coefficients of MA(q).double
mean()
Returns the mean of time series.int
p()
Returns the order of AR.int
q()
Returns the order of MA.double
R2()
Returns R^{2} statistic.double[]
Returns the residuals, that is response minus fitted values.double
RSS()
Returns the residual sum of squares.toString()
double[][]
ttest()
Returns the ttest of the coefficients (including intercept).double
variance()
Returns the residual variance.double[]
x()
Returns the time series.

Constructor Details

ARMA
public ARMA(double[] x, double[] ar, double[] ma, double b, double[] fittedValues, double[] residuals) Constructor. Parameters:
x
 the time seriesar
 the estimated weight parameters of AR(p).ma
 the estimated weight parameters of MA(q).b
 the intercept.fittedValues
 the fitted values.residuals
 the residuals.


Method Details

x
public double[] x()Returns the time series. Returns:
 the time series.

mean
public double mean()Returns the mean of time series. Returns:
 the mean of time series.

p
public int p()Returns the order of AR. Returns:
 the order of AR.

q
public int q()Returns the order of MA. Returns:
 the order of MA.

ttest
public double[][] ttest()Returns the ttest of the coefficients (including intercept). The first column is the coefficients, the second column is the standard error of coefficients, the third column is the tscore of the hypothesis test if the coefficient is zero, the fourth column is the pvalues of test. The last row is of intercept. Returns:
 the ttest of the coefficients.

ar
public double[] ar()Returns the linear coefficients of AR(p). Returns:
 the linear coefficients of AR(p).

ma
public double[] ma()Returns the linear coefficients of MA(q). Returns:
 the linear coefficients of MA(q).

intercept
public double intercept()Returns the intercept. Returns:
 the intercept.

residuals
public double[] residuals()Returns the residuals, that is response minus fitted values. Returns:
 the residuals.

fittedValues
public double[] fittedValues()Returns the fitted values. Returns:
 the fitted values.

RSS
public double RSS()Returns the residual sum of squares. Returns:
 the residual sum of squares.

variance
public double variance()Returns the residual variance. Returns:
 the residual variance.

df
public int df()Returns the degreeoffreedom of residual standard error. Returns:
 the degreeoffreedom of residual standard error.

R2
public double R2()Returns R^{2} statistic. In regression, the R^{2} coefficient of determination is a statistical measure of how well the regression line approximates the real data points. An R^{2} of 1.0 indicates that the regression line perfectly fits the data.In the case of ordinary leastsquares regression, R^{2} increases as we increase the number of variables in the model (R^{2} will not decrease). This illustrates a drawback to one possible use of R^{2}, where one might try to include more variables in the model until "there is no more improvement". This leads to the alternative approach of looking at the adjusted R^{2}.
 Returns:
 R^{2} statistic.

adjustedR2
public double adjustedR2()Returns adjusted R^{2} statistic. The adjusted R^{2} has almost same explanation as R^{2} but it penalizes the statistic as extra variables are included in the model. Returns:
 Adjusted R^{2} statistic.

fit
Fits an ARMA model with HannanRissanen algorithm. Parameters:
x
 the time series.p
 the order of AR.q
 the order of MA. Returns:
 the model.

forecast
public double forecast()Returns 1step ahead forecast. Returns:
 1step ahead forecast.

forecast
public double[] forecast(int l) Returns lstep ahead forecast. Parameters:
l
 the number of steps. Returns:
 lstep ahead forecast.

toString
