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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- 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
- 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+1auto-covariance matrices (from lag 0 tolagMax)
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- 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
- 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
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- 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
pfitted VAR matrices, together with the lower Cholesky triangle of the estimated innovation variance matrix
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- 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 stationaryCheck 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?
- 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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- 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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- Deprecated
(Since version 9)