Dlm

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. implicit def addModel: Semigroup[Model]

   

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

5. final def asInstanceOf[T0]: T0

   

   Definition Classes

   Any

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

   

   Build a block diagonal matrix by combining two matrices of the same size TODO: Test and check this function

7. def clone(): AnyRef

   

   Attributes

   protected[java.lang]

   Definition Classes

   AnyRef

   Annotations

   @throws( ... )

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

   

   Definition Classes

   AnyRef

9. def equals(arg0: Any): Boolean

   

   Definition Classes

   AnyRef → Any

10. def finalize(): Unit

    

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

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

11. def forecast(mod: Model, mt: DenseVector[Double], ct: DenseMatrix[Double], time: Time, p: Parameters): Stream[(Time, Double, Double)]

    

    Forecast a DLM from a state

12. final def getClass(): Class[_]

    

    Definition Classes

    AnyRef → Any

13. def hashCode(): Int

    

    Definition Classes

    AnyRef → Any

14. final def isInstanceOf[T0]: Boolean

    

    Definition Classes

    Any

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

    

    Definition Classes

    AnyRef

16. final def notify(): Unit

    

    Definition Classes

    AnyRef

17. final def notifyAll(): Unit

    

    Definition Classes

    AnyRef

18. def outerSumModel(x: Model, y: Model): Model

    

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

19. def outerSumParameters(x: Parameters, y: Parameters): Parameters

    

20. def polynomial(order: Int): Model

    

21. def regression(x: Array[DenseVector[Double]]): Model

    

    A first order regression model with intercept

22. def rotationMatrix(theta: Double): DenseMatrix[Double]

    

    Build a 2 x 2 rotation matrix

23. def seasonal(period: Int, harmonics: Int): Model

    

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

24. def simStep(mod: Model, x: DenseVector[Double], time: Time, p: Parameters): Rand[(Data, DenseVector[Double])]

    

    Simulate a single step from a DLM

25. def simulate(startTime: Time, mod: Model, p: Parameters): Process[(Data, DenseVector[Double])]

    

    Simulate from a DLM

26. def simulateState(mod: Model, w: DenseMatrix[Double]): Process[(Time, DenseVector[Double])]

    

27. def stepForecast(mod: Model, time: Time, mt: DenseVector[Double], ct: DenseMatrix[Double], p: Parameters): (Time, 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

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

    

    Definition Classes

    AnyRef

29. def toString(): String

    

    Definition Classes

    AnyRef → Any

30. final def wait(): Unit

    

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

32. final def wait(arg0: Long): Unit

    

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )