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.  def apply(y: VectoD, d_: Int = -1, dd: Int = 0, period: Int = 1, mp: Int = 5, mq: Int = 5, mpp: Int = 2, mqq: Int = 2, md: Int = 2, par: Boolean = false, alpha: Double = 0.05): SARIMA

   Automated order selection based on the algorithm proposed in

   Automated order selection based on the algorithm proposed in

   y

   the time series or external regressors to be differenced

   d_

   order of trend differencing

   dd

   order of seasonal differencing

   period

   seasonal period

   mp

   maximum autoregressive order

   mq

   maximum moving average order

   mpp

   maximum seasonal autoregressive order

   mqq

   maximum seasonal moving average order

   md

   maximum order for differencing trend

   par

   flag for running the process in parallel

   See also

   www.jstatsoft.org/article/view/v027i03/v27i03.pdf.

5.  final def asInstanceOf[T0]: T0

   Definition Classes

   Any

6.  def clone(): AnyRef

   Attributes

   protected[java.lang]

   Definition Classes

   AnyRef

   Annotations

   @native() @throws( ... )

7.  def difference(xx: VectoD, d: Int, dd: Int, period: Int): (VectoD, VectoD)

   Difference the time series or external regressors.

   Difference the time series or external regressors. Return both the completely differenced time series and the intermediate (differenced once) time series (needed to scale back results later).

   xx

   the time series or external regressors to be differenced

   d

   the order of simple differencing

   dd

   the order of seasonal differencing

   period

   the seasonal period

8.  def differenceSeason(x_: VectoD, dd: Int, period: Int): (VectoD, VectoD)

   Difference for seasonality.

   Difference for seasonality.

   x_

   the time series or external regressors to be seasonally differenced, this is usually the intermediate result after simple differencing.

   dd

   the order of seasonal differencing

   period

   the seasonal period

9.  def differenceTrend(xx: VectoD, d: Int): VectoD

   Difference for trend.

   Difference for trend.

   xx

   the time series or external regressors to be trend differenced

   d

   the order of simple differencing

10.  final def eq(arg0: AnyRef): Boolean

    Definition Classes

    AnyRef

11.  def equals(arg0: Any): Boolean

    Definition Classes

    AnyRef → Any

12.  def finalize(): Unit

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

    @throws( classOf[java.lang.Throwable] )

13.  final def getClass(): Class[_]

    Definition Classes

    AnyRef → Any

    Annotations

    @native()

14.  def hashCode(): Int

    Definition Classes

    AnyRef → Any

    Annotations

    @native()

15.  final def isInstanceOf[T0]: Boolean

    Definition Classes

    Any

16.  final def ne(arg0: AnyRef): Boolean

    Definition Classes

    AnyRef

17.  final def notify(): Unit

    Definition Classes

    AnyRef

    Annotations

    @native()

18.  final def notifyAll(): Unit

    Definition Classes

    AnyRef

    Annotations

    @native()

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

    Definition Classes

    AnyRef

20.  def toString(): String

    Definition Classes

    AnyRef → Any

21.  def transformBack(xp: VectoD, x_: VectoD, xx: VectoD, d: Int, dd: Int, period: Int): VectoD

    Transform the fitted values on the training data of a differenced time series back to the original scale.

    Transform the fitted values on the training data of a differenced time series back to the original scale.

    xp

    the vector of predictions/fitted values of a differenced time series

    x_

    the intermediate result after differencing for trend, but before differencing for seasonality

    xx

    the original zero-mean time series

    d

    the order of simple differencing

    dd

    the order of seasonal differencing

    period

    the seasonal period

    See also

    stats.stackexchange.com/questions/32634/difference-time-series-before-arima-or-within-arima

22.  def transformBackF(xf: VectoD, x_: VectoD, xx: VectoD, d: Int, dd: Int, period: Int): VectoD

    Transform the forecast values of a differenced time series back to the original scale.

    Transform the forecast values of a differenced time series back to the original scale.

    xf

    the vector of forecasted values of a differenced time series

    x_

    the intermediate result after differencing for trend, but before differencing for seasonality

    xx

    the original zero-mean time series

    d

    the order of simple differencing

    dd

    the order of seasonal differencing

    period

    the seasonal period

    See also

    stats.stackexchange.com/questions/32634/difference-time-series-before-arima-or-within-arima

23.  def transformBackFSeason(xf: VectoD, x_: VectoD, dd: Int, period: Int): VectoD

    Transform the forecast values of a differenced time series back to the original scale.

    Transform the forecast values of a differenced time series back to the original scale. Undo seasonal differencing only.

    xf

    the vector of forecasted values of a differenced time series

    x_

    the intermediate result after differencing for trend, but before differencing for seasonality

    dd

    the order of seasonal differencing

    period

    the seasonal period

    See also

    stats.stackexchange.com/questions/32634/difference-time-series-before-arima-or-within-arima

24.  def transformBackFTrend(xf_: VectoD, xx: VectoD, d: Int): VectoD

    Transform the forecast values of a differenced time series back to the original scale.

    Transform the forecast values of a differenced time series back to the original scale. Undo trend differencing only.

    xf_

    the vector of forecasted values after undoing seasonal differencing

    xx

    the original zero-mean time series

    d

    the order of simple differencing

    See also

    stats.stackexchange.com/questions/32634/difference-time-series-before-arima-or-within-arima

25.  def transformBackSeason(xp: VectoD, x_: VectoD, dd: Int, period: Int): VectoD

    Transform the fitted values on the training data of a differenced time series back to the original scale.

    Transform the fitted values on the training data of a differenced time series back to the original scale. Undo seasonal differencing only.

    xp

    the vector of predictions/fitted values of a differenced time series

    x_

    the intermediate result after differencing for trend, but before differencing for seasonality

    dd

    the order of seasonal differencing

    period

    the seasonal period

    See also

    stats.stackexchange.com/questions/32634/difference-time-series-before-arima-or-within-arima

26.  def transformBackTrend(xp_: VectoD, xx: VectoD, d: Int): VectoD

    Transform the fitted values on the training data of a differenced time series back to the original scale.

    Transform the fitted values on the training data of a differenced time series back to the original scale. Undo trend differencing only.

    xp_

    the vector of predictions/fitted values after undoing seasonal differencing

    xx

    the original zero-mean time series

    d

    the order of simple differencing

    See also

    stats.stackexchange.com/questions/32634/difference-time-series-before-arima-or-within-arima

27.  final def wait(): Unit

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

28.  final def wait(arg0: Long, arg1: Int): Unit

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

29.  final def wait(arg0: Long): Unit

    Definition Classes

    AnyRef

    Annotations

    @native() @throws( ... )