Packages

class ARMA extends Predictor with Error

The ARMA class provide basic time series analysis capabilities for Auto- Regressive 'AR' and Moving Average 'MA' models. In an 'ARMA(p, q)' model, 'p' and 'q' refer to the order of the Auto-Regressive and Moving Average components of the model. ARMA models are often used for forecasting.

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Instance Constructors

  1. new ARMA(y: VectorD, t: VectorD)

    y

    the input vector (time series data)

    t

    the time vector

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. val acf: VectorD
  5. def ar(phi: VectorD): VectorD

    Return a vector that is the predictions of a 'p'th order Auto-Regressive 'AR(p)) model.

    Return a vector that is the predictions of a 'p'th order Auto-Regressive 'AR(p)) model. 'f_t = phi_0 * x_t-1 + ... + phi_p-1 * x_t-p + e_t'

    phi

    the estimated 'AR(p)' coefficients

  6. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  7. val b: VectoD

    Coefficient/parameter vector [b_0, b_1, ...

    Coefficient/parameter vector [b_0, b_1, ... b_k]

    Attributes
    protected
    Definition Classes
    Predictor
  8. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  9. def coefficient: VectoD

    Return the vector of coefficient/parameter values.

    Return the vector of coefficient/parameter values.

    Definition Classes
    Predictor
  10. def durbinLevinson: MatrixD

    Apply the Durbin-Levinson Algorithm to iteratively compute the 'psi' matrix.

    Apply the Durbin-Levinson Algorithm to iteratively compute the 'psi' matrix. The last row of the matrix gives 'AR' coefficients.

    See also

    www.stat.tamu.edu/~suhasini/teaching673/time_series.pdf

  11. val e: VectoD

    Residual/error vector [e_0, e_1, ...

    Residual/error vector [e_0, e_1, ... e_m-1]

    Attributes
    protected
    Definition Classes
    Predictor
  12. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  13. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  14. def est_ar(p: Int = 1): VectorD

    Estimate the coefficients for a 'p'th order Auto-Regressive 'AR(p)' model.

    Estimate the coefficients for a 'p'th order Auto-Regressive 'AR(p)' model. 'x_t = phi_0 * x_t-1 + ... + phi_p-1 * x_t-p + e_t' Uses the Durbin-Levinson Algorithm to determine the coefficients. The 'phi' vector is 'p'th row of 'psi' matrix (ignoring the first (0th) column).

    p

    the order of the 'AR' model

  15. def est_ma(q: Int = 1): VectorD

    Estimate the coefficients for a 'q'th order a Moving Average(q) model.

    Estimate the coefficients for a 'q'th order a Moving Average(q) model. x_t = e_t - theta_0 * e_t-1 - ... - theta_q-1 * e_t-q FIX: use Method of Innovations

  16. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  17. def fit: VectorD

    Return the quality of fit including 'rSquared'.

    Return the quality of fit including 'rSquared'.

    Definition Classes
    ARMAPredictor
  18. def fitLabels: Array[String]

    Return the labels for the fit.

    Return the labels for the fit. Override when necessary.

    Definition Classes
    Predictor
  19. final def flaw(method: String, message: String): Unit

    Show the flaw by printing the error message.

    Show the flaw by printing the error message.

    method

    the method where the error occurred

    message

    the error message

    Definition Classes
    Error
  20. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
  21. def hashCode(): Int
    Definition Classes
    AnyRef → Any
  22. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  23. def ma(theta: VectorD): VectorD

    Return a vector that is the predictions of a 'q'th order Moving Average (MA) model.

    Return a vector that is the predictions of a 'q'th order Moving Average (MA) model. x_t = e_t + theta_0 * e_t-1 + ... + theta_q-1 * e_t-q

    theta

    the estimated MA(q) coefficients

  24. val mu: Double
  25. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  26. final def notify(): Unit
    Definition Classes
    AnyRef
  27. final def notifyAll(): Unit
    Definition Classes
    AnyRef
  28. var pacf: VectorD
  29. def plotFunc(fVec: VectorD, name: String): Unit

    Plot a function, e.g., Auto-Correlation Function 'ACF', Partial Auto-Correlation Function 'PACF'.

    Plot a function, e.g., Auto-Correlation Function 'ACF', Partial Auto-Correlation Function 'PACF'.

    fVec

    the vector given function values

    name

    the name of the function

  30. def predict(y: VectoD): Double

    For all the time points in vector t, predict the value of y = f(t) by ...

    For all the time points in vector t, predict the value of y = f(t) by ...

    Definition Classes
    ARMAPredictor
  31. def predict(z: VectorI): Double

    Given a new discrete data vector z, predict the y-value of f(z).

    Given a new discrete data vector z, predict the y-value of f(z).

    z

    the vector to use for prediction

    Definition Classes
    Predictor
  32. def residual: VectoD

    Return the vector of residuals/errors.

    Return the vector of residuals/errors.

    Definition Classes
    Predictor
  33. val sig2: Double
  34. def smooth(l: Int): VectorD

    Smooth the 'y' vector by taking the 'l'th order moving average.

    Smooth the 'y' vector by taking the 'l'th order moving average.

    l

    the number of points to average

  35. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  36. def toString(): String
    Definition Classes
    AnyRef → Any
  37. def train(): Unit

    Fit an ARMA model to times series data.

    Fit an ARMA model to times series data.

    Definition Classes
    ARMAPredictor
  38. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  39. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  40. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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Inherited from Predictor

Inherited from AnyRef

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