Description Usage Arguments Details Value Author(s) References See Also Examples

Uses Kalman Smoothing on structural time series models (or on the state space representation of an arima model) for imputation.

1 |

`x` |
Numeric Vector ( |

`model` |
Model to be used. With this parameter the State Space Model (on which KalmanSmooth is performed) can be chosen. Accepts the following input: "auto.arima" - For using the state space representation of arima model (using auto.arima) "StructTS" - For using a structural model fitted by maximum likelihood (using StructTS)
For both auto.arima and StructTS additional parameters for model building can be given with the ... parameter Additionally it is also possible to use a user created state space model (See code Example 5). This state space model could for example be obtained from another R package for structural time series modeling. Furthermore providing the state space representation of a arima model from arima is also possible. But it is important to note, that user created state space models must meet the requirements specified under KalmanLike. This means the user supplied state space model has to be in form of a list with at least components T, Z, h , V, a, P, Pn. (more details under KalmanLike) |

`smooth` |
if |

`nit` |
Parameter from Kalman Filtering (see KalmanLike). Usually no need to change from default. |

`...` |
Additional parameters to be passed through to the functions that build the State Space Models (StructTS or auto.arima). |

The KalmanSmoother used in this function is `KalmanSmooth`

.
It operates either on a `Basic Structural Model`

obtained by `StructTS`

or
the state space representation of a ARMA model obtained by `auto.arima`

.

For an detailed explanation of Kalman Filtering and Space Space Models the following literature is a good starting point:

G. Welch, G. Bishop, An Introduction to the Kalman Filter. SIGGRAPH 2001 Course 8, 1995

Harvey, Andrew C. Forecasting, structural time series models and the Kalman filter. Cambridge university press, 1990

Grewal, Mohinder S. Kalman filtering. Springer Berlin Heidelberg, 2011

Vector (`vector`

) or Time Series (`ts`

) object (dependent on given input at parameter x)

Steffen Moritz

Hyndman RJ and Khandakar Y (2008). "Automatic time series forecasting: the forecast package for R". Journal of Statistical Software, 26(3).

`na.interpolation`

,
`na.locf`

,
`na.ma`

, `na.mean`

,
`na.random`

, `na.replace`

,
`na.seadec`

, `na.seasplit`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ```
#Example 1: Perform imputation with KalmanSmoother and state space representation of arima model
na.kalman(tsAirgap)
#Example 2: Perform imputation with KalmanRun and state space representation of arima model
na.kalman(tsAirgap, smooth = FALSE)
#Example 3: Perform imputation with KalmanSmooth and StructTS model
na.kalman(tsAirgap, model ="StructTS", smooth = TRUE)
#Example 4: Perform imputation with KalmanSmooth and StructTS model with additional parameters
na.kalman(tsAirgap, model ="StructTS", smooth = TRUE, type ="trend")
#Example 5: Perform imputation with KalmanSmooth and user created model
usermodel <- arima(tsAirgap,order = c(1,0,1))$model
na.kalman(tsAirgap,model = usermodel)
#Example 6: Same as example 1, just written with pipe operator
tsAirgap %>% na.kalman
``` |

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