A single observation of a model
Definition of a DLM
Parameters of a DLM
Get the angle of the rotation for the seasonal model
Define a discrete time univariate first order autoregressive model
Define a discrete time univariate first order autoregressive model
a sequence of autoregressive parameters of length equal to the order of the autoregressive state
Build a block diagonal matrix by combining two matrices of the same size
Dynamic Linear Models can be combined in order to model different time dependent phenomena, for instance seasonal with trend
Forecast a DLM from a state
Forecast a DLM from a state
a DLM
the posterior mean of the state at time t (start of forecast)
the posterior variance of the state at time t (start of forecast)
the starting time of the forecast
the parameters of the DLM
a Stream of forecasts
Similar Dynamic Linear Models can be combined in order to model multiple similar times series in a vectorised way
A polynomial model
A first order regression model with intercept
A first order regression model with intercept
an array of covariates
Build a 2 x 2 rotation matrix
Create a seasonal model with fourier components in the system evolution matrix
Create a seasonal model with fourier components in the system evolution matrix
the period of the seasonality
the number of harmonics in the seasonal model
a seasonal DLM model
Build the G matrix for the system evolution
Simulate a single step from a DLM, used in simulateRegular
Simulate a single step from a DLM, used in simulateRegular
a DLM model
a realisation from the latent state at time t-1
the current time
the parameters of the DLM model
the time increment between successive realisations of the process
Simulate from a DLM at the given times
Simulate from a DLM
Simulate the state at the given times
Simulate the latent-state from a DLM model
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
a DLM specification
the current time
the mean of the latent state at time t
the variance of the latent state at time t
the parameters of the DLM