scalation.analytics

ARMA

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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Error, Predictor, AnyRef, Any
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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 pth order Auto-Regressive (AR(p)) model.

    Return a vector that is the predictions of a pth 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. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  8. 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

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

  9. final def eq(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  10. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any
  11. def est_ar(p: Int = 1): VectorD

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

    Estimate the coefficients for a pth 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 pth row of psi matrix (ignoring the first (0th) column).

    p

    the order of the AR model

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

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

    Estimate the coefficients for a qth 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

  13. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  14. 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
  15. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any
  16. def hashCode(): Int

    Definition Classes
    AnyRef → Any
  17. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any
  18. def ma(theta: VectorD): VectorD

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

    Return a vector that is the predictions of a qth 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

  19. val mu: Double

  20. final def ne(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  21. final def notify(): Unit

    Definition Classes
    AnyRef
  22. final def notifyAll(): Unit

    Definition Classes
    AnyRef
  23. var pacf: VectorD

  24. def plotFunc(fVec: VectorD, name: String): Unit

    Plot a function, e.

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

    fVec

    the vector given function values

    name

    the name of the function

  25. def predict(z: MatrixD): VectorD

    Given several time vectors, forecast the y-values.

    Given several time vectors, forecast the y-values.

    z

    the matrix containing row time-vectors to use for prediction

    Definition Classes
    ARMAPredictor
  26. def predict(y: VectorD): 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
  27. 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
  28. val sig2: Double

  29. def smooth(l: Int): VectorD

    Smooth the y vector by taking the lth order moving average.

    Smooth the y vector by taking the lth order moving average.

    l

    the number of points to average

  30. final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes
    AnyRef
  31. def toString(): String

    Definition Classes
    AnyRef → Any
  32. def train(): Unit

    Fit an ARMA model to times series data.

    Fit an ARMA model to times series data.

    Definition Classes
    ARMAPredictor
  33. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  34. final def wait(arg0: Long, arg1: Int): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  35. final def wait(arg0: Long): Unit

    Definition Classes
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    Annotations
    @throws( ... )

Inherited from Error

Inherited from Predictor

Inherited from AnyRef

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