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 angle(period: Int)(dt: Double): Double

   Get the angle of the rotation for the seasonal model

5.  final def asInstanceOf[T0]: T0

   Definition Classes

   Any

6.  def autoregressive(phi: Double*): Dlm

   Define a discrete time univariate autoregressive model

   Define a discrete time univariate autoregressive model

   phi

   a sequence of autoregressive parameters of length equal to the order of the autoregressive state

7.  def blockDiagonal(a: DenseMatrix[Double], b: DenseMatrix[Double]): DenseMatrix[Double]

   Build a block diagonal matrix by combining two matrices of the same size

8.  def clone(): AnyRef

   Attributes

   protected[java.lang]

   Definition Classes

   AnyRef

   Annotations

   @native() @throws( ... )

9.  def composeModels(x: Dlm, y: Dlm): Dlm

   Dynamic Linear Models can be combined in order to model different time dependent phenomena, for instance seasonal with trend

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.  def forecast(mod: Dlm, mt: DenseVector[Double], ct: DenseMatrix[Double], time: Double, p: DlmParameters): Stream[(Double, DenseVector[Double], DenseMatrix[Double])]

    Forecast a DLM from a state

    Forecast a DLM from a state

    mod

    a DLM

    mt

    the posterior mean of the state at time t (start of forecast)

    ct

    the posterior variance of the state at time t (start of forecast)

    time

    the starting time of the forecast

    p

    the parameters of the DLM

    returns

    a Stream containing the time, forecast mean and variance

14.  final def getClass(): Class[_]

    Definition Classes

    AnyRef → Any

    Annotations

    @native()

15.  def hashCode(): Int

    Definition Classes

    AnyRef → Any

    Annotations

    @native()

16.  def initialiseState(model: Dlm, params: DlmParameters): (Data, DenseVector[Double])
17.  final def isInstanceOf[T0]: Boolean

    Definition Classes

    Any

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

    Definition Classes

    AnyRef

19.  final def notify(): Unit

    Definition Classes

    AnyRef

    Annotations

    @native()

20.  final def notifyAll(): Unit

    Definition Classes

    AnyRef

    Annotations

    @native()

21.  def observation(model: Dlm, p: DlmParameters, x: DenseVector[Double], time: Double): Rand[DenseVector[Double]]
22.  def outerSumModel(x: Dlm, y: Dlm): Dlm

    Similar Dynamic Linear Models can be combined in order to model multiple similar times series in a vectorised way

23.  def outerSumParameters(x: DlmParameters, y: DlmParameters): DlmParameters

    Combine parameters of univariate models appropriately for a multivariate model

24.  def polynomial(order: Int): Dlm

    A polynomial model

25.  def regression(x: Array[DenseVector[Double]]): Dlm

    A first order regression model with intercept

    A first order regression model with intercept

    x

    an array of covariates

26.  def rotationMatrix(theta: Double): DenseMatrix[Double]

    Build a 2 x 2 rotation matrix

27.  def seasonal(period: Int, harmonics: Int): Dlm

    Create a seasonal model with fourier components in the system evolution matrix

    Create a seasonal model with fourier components in the system evolution matrix

    period

    the period of the seasonality

    harmonics

    the number of harmonics in the seasonal model

    returns

    a seasonal DLM model

28.  def seasonalG(period: Int, harmonics: Int)(dt: Double): DenseMatrix[Double]

    Build the G matrix for the system evolution

29.  def simStep(model: Dlm, params: DlmParameters)(state: DenseVector[Double], time: Double, dt: Double): Rand[(Data, DenseVector[Double])]
30.  def simulateRegular(model: Dlm, params: DlmParameters, dt: Double): Process[(Data, DenseVector[Double])]
31.  def stepForecast(mod: Dlm, time: Double, dt: Double, mt: DenseVector[Double], ct: DenseMatrix[Double], p: DlmParameters): (Double, DenseVector[Double], DenseMatrix[Double], DenseVector[Double], DenseMatrix[Double])

    Perform a single forecast step, equivalent to performing the Kalman Filter Without an observation of the process

    Perform a single forecast step, equivalent to performing the Kalman Filter Without an observation of the process

    mod

    a DLM specification

    time

    the current time

    mt

    the mean of the latent state at time t

    ct

    the variance of the latent state at time t

    p

    the parameters of the DLM

32.  def stepState(model: Dlm, p: DlmParameters, state: DenseVector[Double], dt: Double): Rand[DenseVector[Double]]
33.  def summariseForecast(interval: Double)(ft: DenseVector[Double], qt: DenseMatrix[Double]): List[List[Double]]

    Summarise forecast

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

    Definition Classes

    AnyRef

35.  def toString(): String

    Definition Classes

    AnyRef → Any

36.  final def wait(): Unit

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    Definition Classes

    AnyRef

    Annotations

    @native() @throws( ... )