Dlm

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

   

   Get the angle of the rotation for the seasonal model

5. final def asInstanceOf[T0]: T0

   

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

   

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

7. def clone(): AnyRef

   

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8. def composeModels(x: Model, y: Model): Model

   

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

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

   

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10. def equals(arg0: Any): Boolean

    

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11. def finalize(): Unit

    

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

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

    

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14. def hashCode(): Int

    

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15. final def isInstanceOf[T0]: Boolean

    

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

    

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17. final def notify(): Unit

    

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18. final def notifyAll(): Unit

    

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19. 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

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

    

21. def polynomial(order: Int): Model

    

    A polynomial model

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

    

    A first order regression model with intercept

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

    

    Build a 2 x 2 rotation matrix

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

    

    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

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

    

    Build the G matrix for the system evolution

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

    

    Simulate a single step from a DLM, used in simulateRegular

    Simulate a single step from a DLM, used in simulateRegular

    mod

    a DLM model

    x

    a realisation from the latent state at time t-1

    time

    the current time

    p

    the parameters of the DLM model

    dt

    the time increment between successive realisations of the process

27. def simulate(times: Iterable[Double], mod: Model, p: Parameters): Iterable[(Data, DenseVector[Double])]

    

    Simulate from a DLM at the given times

28. def simulateRegular(startTime: Double, mod: Model, p: Parameters, dt: Double): Process[(Data, DenseVector[Double])]

    

    Simulate from a DLM

29. def simulateState(times: Iterable[Double], g: (Double) ⇒ DenseMatrix[Double], p: Parameters, init: (Double, DenseVector[Double])): Iterable[(Double, DenseVector[Double])]

    

    Simulate the state at the given times

30. def simulateStateRegular(mod: Model, w: DenseMatrix[Double]): Process[(Double, DenseVector[Double])]

    

    Simulate the latent-state from a DLM model

31. def stepForecast(mod: Model, time: Double, dt: Double, mt: DenseVector[Double], ct: DenseMatrix[Double], p: Parameters): (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. final def synchronized[T0](arg0: ⇒ T0): T0

    

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33. def toString(): String

    

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34. final def wait(): Unit

    

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

    

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

    

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