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scalaglm

TimeSeries

object TimeSeries

Utilities for multivariate time series

These utilities assume that the time series is stored in a Breeze DMD with variables/component series in columns, and observations in rows with the first row corresponding to the first observation and the last row to the last.

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  5. def autocovariance(x: DMD, lag: Int, centre: Boolean = true): DMD

    Compute the auto-covariance of the time series at a given lag

    Compute the auto-covariance of the time series at a given lag

    x

    Time series

    lag

    The lag at which the auto-covariance is to be computed (can be negative)

    centre

    Does the time series need to be centred?

    returns

    Auto-covariance matrix

  6. def autocovariances(x: DMD, lagMax: Int, centre: Boolean = true): List[DMD]

    Compute a set of auto-covariance matrices

    Compute a set of auto-covariance matrices

    x

    Time series

    lagMax

    The maximum lag required

    centre

    Does the time series need to be centred?

    returns

    List of lagMax+1 auto-covariance matrices (from lag 0 to lagMax)

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  8. def correlationMat(x: DMD, centre: Boolean = true): DMD

    The correlation matrix of a time series

    The correlation matrix of a time series

    x

    A time series

    centre

    Does the time series need to be centred?

    returns

    Correlation matrix

  9. def covariance(x: DMD, y: DMD, centre: Boolean = true): DMD

    Compute the covariance between two time series of the same length

    Compute the covariance between two time series of the same length

    x

    A time series

    y

    Another time series

    centre

    Do the series need to be centred, first?

    returns

    Covariance matrix

  10. final def eq(arg0: AnyRef): Boolean
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  12. def fitVar(x: DMD, p: Int, centre: Boolean = true): (List[DMD], DMD)

    Fit a mean-zero VAR(p) model to a time series via least squares.

    Fit a mean-zero VAR(p) model to a time series via least squares. Since the model being fit is mean zero, you may want to mean-centre your data.

    x

    Time series

    p

    The order of the VAR(p) model to be fits

    centre

    Mean-centre the time series before fitting?

    returns

    The list of p fitted VAR matrices, together with the lower Cholesky triangle of the estimated innovation variance matrix

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  16. def isStat(phi: List[DMD]): Boolean

    Check whether a given set of VAR matrices (such as returned by fitVar) correspond to a VAR(p) model that is stationary

    Check whether a given set of VAR matrices (such as returned by fitVar) correspond to a VAR(p) model that is stationary

    phi

    List of VAR matrices

    returns

    Is this model stationary?

  17. def meanCentre(x: DMD): DMD

    Sweep out the mean of each component series

    Sweep out the mean of each component series

    x

    A multivariate time series

    returns

    A mean zero time series

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  23. def varianceMat(x: DMD, centre: Boolean = true): DMD

    The variance matrix of a time series

    The variance matrix of a time series

    x

    A time series

    centre

    Does the time series need to be centred?

    returns

    Variance matrix

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