forecast

Integer which states how many observations is forecasted.

In case where matrix Z is not time-invariant, p*m*fc array
  of matrix Zt, t=n+1,...,n+fc.

Zt.fc

In case where matrix T is not time-invariant, m*m*fc array
  of matrix Tt, t=n+1,...,n+fc.

Tt.fc

In case where matrix R is not time-invariant, m*r*fc array
  of matrix Rt, t=n+1,...,n+fc.

Rt.fc

In case where matrix H is not time-invariant, p*p*fc array
  of matrix Ht, t=n+1,...,n+fc.

Ht.fc

In case where matrix Q is not time-invariant, r*r*fc array
  of matrix Qt, t=n+1,...,n+fc.

Qt.fc

Performs forecasting using output from function 'kf' (Kalman filter).

Package KFAS provides functions for Kalman filtering,
        state, disturbance and simulation smoothing, forecasting and
        simulation of state space models. All functions can use exact
        diffuse initialisation when distributions of some or all
        elements of initial state vector are unknown. Filtering, state
        smoothing and simulation functions use sequential processing
        algorithm, which is faster than standard approach, and it also
        allows singularity of prediction error variance matrix. KFAS
        also contains function for computing the likelihood of
        exponential family state space models and function for state
        smoothing of exponential family state space models.

forecast: Forecast state space model

Description

Usage

Arguments

Value

Details