StructTS {stats} R Documentation

## Fit Structural Time Series

### Description

Fit a structural model for a time series by maximum likelihood.

### Usage

StructTS(x, type = c("level", "trend", "BSM"), init = NULL,
fixed = NULL, optim.control = NULL)


### Arguments

 x a univariate numeric time series. Missing values are allowed. type the class of structural model. If omitted, a BSM is used for a time series with frequency(x) > 1, and a local trend model otherwise. Can be abbreviated. init initial values of the variance parameters. fixed optional numeric vector of the same length as the total number of parameters. If supplied, only NA entries in fixed will be varied. Probably most useful for setting variances to zero. optim.control List of control parameters for optim. Method "L-BFGS-B" is used.

### Details

Structural time series models are (linear Gaussian) state-space models for (univariate) time series based on a decomposition of the series into a number of components. They are specified by a set of error variances, some of which may be zero.

The simplest model is the local level model specified by type = "level". This has an underlying level \mu_t which evolves by

\mu_{t+1} = \mu_t + \xi_t, \qquad \xi_t \sim N(0, \sigma^2_\xi)

The observations are

x_t = \mu_t + \epsilon_t, \qquad \epsilon_t \sim N(0, \sigma^2_\epsilon)

There are two parameters, \sigma^2_\xi and \sigma^2_\epsilon. It is an ARIMA(0,1,1) model, but with restrictions on the parameter set.

The local linear trend model, type = "trend", has the same measurement equation, but with a time-varying slope in the dynamics for \mu_t, given by

 \mu_{t+1} = \mu_t + \nu_t + \xi_t, \qquad \xi_t \sim N(0, \sigma^2_\xi) 

 \nu_{t+1} = \nu_t + \zeta_t, \qquad \zeta_t \sim N(0, \sigma^2_\zeta) 

with three variance parameters. It is not uncommon to find \sigma^2_\zeta = 0 (which reduces to the local level model) or \sigma^2_\xi = 0, which ensures a smooth trend. This is a restricted ARIMA(0,2,2) model.

The basic structural model, type = "BSM", is a local trend model with an additional seasonal component. Thus the measurement equation is

x_t = \mu_t + \gamma_t + \epsilon_t, \qquad \epsilon_t \sim N(0, \sigma^2_\epsilon)

where \gamma_t is a seasonal component with dynamics

 \gamma_{t+1} = -\gamma_t + \cdots + \gamma_{t-s+2} + \omega_t, \qquad \omega_t \sim N(0, \sigma^2_\omega) 

The boundary case \sigma^2_\omega = 0 corresponds to a deterministic (but arbitrary) seasonal pattern. (This is sometimes known as the ‘dummy variable’ version of the BSM.)

### Value

A list of class "StructTS" with components:

 coef the estimated variances of the components. loglik the maximized log-likelihood. Note that as all these models are non-stationary this includes a diffuse prior for some observations and hence is not comparable to arima nor different types of structural models. loglik0 the maximized log-likelihood with the constant used prior to R 3.0.0, for backwards compatibility. data the time series x. residuals the standardized residuals. fitted a multiple time series with one component for the level, slope and seasonal components, estimated contemporaneously (that is at time t and not at the end of the series). call the matched call. series the name of the series x. code the convergence code returned by optim. model, model0 Lists representing the Kalman Filter used in the fitting. See KalmanLike. model0 is the initial state of the filter, model its final state. xtsp the tsp attributes of x.

### Note

Optimization of structural models is a lot harder than many of the references admit. For example, the AirPassengers data are considered in Brockwell & Davis (1996): their solution appears to be a local maximum, but nowhere near as good a fit as that produced by StructTS. It is quite common to find fits with one or more variances zero, and this can include \sigma^2_\epsilon.

### References

Brockwell, P. J. & Davis, R. A. (1996). Introduction to Time Series and Forecasting. Springer, New York. Sections 8.2 and 8.5.

Durbin, J. and Koopman, S. J. (2001) Time Series Analysis by State Space Methods. Oxford University Press.

Harvey, A. C. (1989) Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.

Harvey, A. C. (1993) Time Series Models. 2nd Edition, Harvester Wheatsheaf.

KalmanLike, tsSmooth; stl for different kind of (seasonal) decomposition.

### Examples

## see also JohnsonJohnson, Nile and AirPassengers
require(graphics)

trees <- window(treering, start = 0)
(fit <- StructTS(trees, type = "level"))
plot(trees)
lines(fitted(fit), col = "green")
tsdiag(fit)

(fit <- StructTS(log10(UKgas), type = "BSM"))
par(mfrow = c(4, 1)) # to give appropriate aspect ratio for next plot.
plot(log10(UKgas))
plot(cbind(fitted(fit), resids=resid(fit)), main = "UK gas consumption")

## keep some parameters fixed; trace optimizer:
StructTS(log10(UKgas), type = "BSM", fixed = c(0.1,0.001,NA,NA),
optim.control = list(trace = TRUE))


[Package stats version 4.3.0 Index]