Object

dlm.model

Dlm

Related Doc: package model

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object Dlm

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Type Members

  1. case class Model(f: ObservationMatrix, g: SystemMatrix) extends Product with Serializable

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    Definition of a DLM

  2. type Observation = DenseVector[Double]

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  3. type ObservationMatrix = (Time) ⇒ DenseMatrix[Double]

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  4. case class Parameters(v: DenseMatrix[Double], w: DenseMatrix[Double], m0: DenseVector[Double], c0: DenseMatrix[Double]) extends Product with Serializable

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    Parameters of a DLM

  5. type State = MultivariateGaussian

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  6. type SystemMatrix = (Time) ⇒ DenseMatrix[Double]

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  7. type Time = Int

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Value Members

  1. final def !=(arg0: Any): Boolean

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  2. final def ##(): Int

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  3. final def ==(arg0: Any): Boolean

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  4. implicit def addModel: Semigroup[Model]

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    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

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

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

  7. def clone(): AnyRef

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

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  9. def equals(arg0: Any): Boolean

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  10. def finalize(): Unit

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

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  12. def hashCode(): Int

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  13. final def isInstanceOf[T0]: Boolean

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

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  15. final def notify(): Unit

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  16. final def notifyAll(): Unit

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

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

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

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  19. def polynomial(order: Int): Model

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

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    A first order regression model with intercept

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

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    Build a 2 x 2 rotation matrix

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

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

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

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    Simulate a single step from a DLM

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

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    Simulate from a DLM

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

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

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  27. def toString(): String

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  28. final def wait(): Unit

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

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

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