A collection and description of simple to
use functions to model univariate autoregressive
moving average time series processes, including
time series simulation, parameter estimation,
diagnostic analysis of the fit, and predictions
of future values.

The functions are:

`armaSim` | Simulates an artificial ARMA time series process, |

`armaFit` | Fits the parameters of an ARMA time series process, |

`print` | Print Method, |

`plot` | Plot Method, |

`summary` | Summary Method, |

`predict` | Forecasts and optionally plots an ARMA process, |

`fitted` | Method, returns fitted values, |

`coef|coefficients` | Method, returns coefficients, |

`residuals` | Method, returns residuals. |

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | ```
armaSim(model = list(ar = c(0.5, -0.5), d = 0, ma = 0.1), n = 100,
innov = NULL, n.start = 100, start.innov = NULL,
rand.gen = rnorm, rseed = NULL, addControl = FALSE, ...)
armaFit(formula, data, method = c("mle", "ols"), include.mean = TRUE,
fixed = NULL, title = NULL, description = NULL, ...)
## S4 method for signature 'fARMA'
show(object)
## S3 method for class 'fARMA'
plot(x, which = "ask", gof.lag = 10, ...)
## S3 method for class 'fARMA'
summary(object, doplot = TRUE, which = "all", ...)
## S3 method for class 'fARMA'
predict(object, n.ahead = 10, n.back = 50, conf = c(80, 95),
doplot = TRUE, ...)
## S3 method for class 'fARMA'
fitted(object, ...)
## S3 method for class 'fARMA'
coef(object, ...)
## S3 method for class 'fARMA'
residuals(object, ...)
``` |

`addControl` |
[armaSim] - |

`data` |
an optional timeSeries or data frame object containing the variables
in the model. If not found in |

`description` |
a character string which allows for a brief description. |

`doplot` |
[armaRoots] - |

`fixed` |
[armaFit] - |

`formula` |
[armaFit] - |

`gof.lag` |
[print][plot][summary][predict] - |

`include.mean` |
[armaFit] - |

`innov` |
[armaSim] - |

`method` |
[armaFit] - |

`model` |
[armaSim] - |

`n` |
[armaSim] - |

`n.ahead, n.back, conf` |
[print][plot][summary][predict] - |

`n.start` |
[armaSim] - |

`object` |
[summary][predict] - |

`rand.gen` |
[armaSim] - |

`rseed` |
[armaSim] - |

`start.innov` |
[armaSim] - |

`title` |
a character string which allows for a project title. |

`which` |
[plot][summary] - |

`x` |
[print][plot] - |

`...` |
additional arguments to be passed to the output timeSeries. (charvec, units, ...) |

**AR - Auto-Regressive Modelling:**

The argument `x~ar(p)`

calls the underlying functions
`ar.mle`

or `ar.ols`

depending on the
`method`

's choice.
For definiteness, the AR models are defined through

*\code{(x[t] - m) = a[1]*(x[t-1] - m) + … + a[p]*(x[t-p] - m) + e[t]}*

Order selection can be achieved through the comparison of AIC
values for different model specifications. However this may be
problematic, as of the methods here only `ar.mle`

performs
true maximum likelihood estimation. The AIC is computed as if
the variance estimate were the MLE, omitting the determinant
term from the likelihood. Note that this is not the same as the
Gaussian likelihood evaluated at the estimated parameter values.
With `method="yw"`

the variance matrix of the innovations is
computed from the fitted coefficients and the autocovariance of
`x`

. Burg's method allows for two alternatives
`method="burg1"`

or `method="burg2"`

to estimate the
innovations variance and hence AIC. Method 1 is to use the update
given by the Levinson-Durbin recursion (Brockwell and Davis, 1991),
and follows S-PLUS. Method 2 is the mean of the sum of squares of
the forward and backward prediction errors (as in Brockwell and Davis,
1996). Percival and Walden (1998) discuss both.

`[stats:ar]`

**MA - Moving-Average Modelling:**

The argument `x~ma(q)`

maps the call to the
argument `x ~ arima(0, 0, q)`

.

**ARMA - Auto-Regressive Moving-Average Modelling:**

The argument `x~arma(p,q)`

maps the call to the
argument `x~arima(p, 0, q)`

.

**ARIMA - Integrated ARMA Modelling:**

The argument `x~arima()`

calls the underlying function
`arima`

from **R**'s `ts`

package. For definiteness, the AR
models are defined through

*\code{x[t] = a[1]x[t-1] + … + a[p]x[t-p] + e[t] + b[1]e[t-1] + … + b[q]e[t-q]}*

and so the MA coefficients differ in sign from those of
S-PLUS. Further, if `include.mean`

is `TRUE`

, this formula
applies to *x-m* rather than *x*. For ARIMA models with
differencing, the differenced series follows a zero-mean ARMA model.

The variance matrix of the estimates is found from the Hessian of
the log-likelihood, and so may only be a rough guide.

Optimization is done by `optim`

. It will work
best if the columns in `xreg`

are roughly scaled to zero mean
and unit variance, but does attempt to estimate suitable scalings.
The exact likelihood is computed via a state-space representation
of the ARIMA process, and the innovations and their variance found
by a Kalman filter. The initialization of the differenced ARMA
process uses stationarity. For a differenced process the
non-stationary components are given a diffuse prior (controlled
by `kappa`

). Observations which are still controlled by the
diffuse prior (determined by having a Kalman gain of at least
`1e4`

) are excluded from the likelihood calculations. (This
gives comparable results to `arima0`

in the absence
of missing values, when the observations excluded are precisely those
dropped by the differencing.)

Missing values are allowed, and are handled exactly in method `"ML"`

.

If `transform.pars`

is true, the optimization is done using an
alternative parametrization which is a variation on that suggested by
Jones (1980) and ensures that the model is stationary. For an AR(p)
model the parametrization is via the inverse tanh of the partial
autocorrelations: the same procedure is applied (separately) to the
AR and seasonal AR terms. The MA terms are not constrained to be
invertible during optimization, but they will be converted to
invertible form after optimization if `transform.pars`

is true.

Conditional sum-of-squares is provided mainly for expositional
purposes. This computes the sum of squares of the fitted innovations
from observation `n.cond`

on, (where `n.cond`

is at least
the maximum lag of an AR term), treating all earlier innovations to
be zero. Argument `n.cond`

can be used to allow comparability
between different fits. The “part log-likelihood” is the first
term, half the log of the estimated mean square. Missing values
are allowed, but will cause many of the innovations to be missing.

When regressors are specified, they are orthogonalized prior to
fitting unless any of the coefficients is fixed. It can be helpful to
roughly scale the regressors to zero mean and unit variance.

Note from `arima`

: The functions parse their arguments to the
original time series functions available in **R**'s time series library
`ts`

.

The results are likely to be different from S-PLUS's
`arima.mle`

, which computes a conditional likelihood and does
not include a mean in the model. Further, the convention used by
`arima.mle`

reverses the signs of the MA coefficients.

`[stats:arima]`

**ARFIMA/FRACDIFF Modelling:**

The argument `x~arfima()`

calls the underlying functions from
**R**'s `fracdiff`

package. The estimator calculates the maximum
likelihood estimators of the parameters of a fractionally-differenced
ARIMA (p,d,q) model, together (if possible) with their estimated
covariance and correlation matrices and standard errors, as well
as the value of the maximized likelihood. The likelihood is
approximated using the fast and accurate method of Haslett and
Raftery (1989). Note, the number of AR and MA coefficients should
not be too large (say < 10) to avoid degeneracy in the model.

The optimization is carried out in two levels: an outer univariate
unimodal optimization in d over the interval [0,.5], and an inner
nonlinear least-squares optimization in the AR and MA parameters to
minimize white noise variance.

`[fracdiff:fracdiff]`

`armaFit`

returns an S4 object of class `"fARMA"`

, with the following
slots:

`call` |
the matched function call. |

`data` |
the input data in form of a data.frame. |

`description` |
allows for a brief project description. |

`fit` |
the results as a list returned from the underlying time series model function. |

`method` |
the selected time series model naming the applied method. |

`formula` |
the formula expression describing the model. |

`parameters` |
named parameters or coefficients of the fitted model. |

`title` |
a title string. |

There is nothing really new in this package. The benefit you will get with this collection is, that all functions have a common argument list with a formula to specify the model and presetted arguments for the specification of the algorithmic method. For users who have already modeled GARCH processes with R/Rmetrics and SPlus/Finmetrics, this approach will be quite natural.

The function `armaFit`

allows for the following formula arguments:

`x ~ ar()` | autoregressive time series processes, |

`x ~ ma()` | moving average time series processes, |

`x ~ arma()` | autoregressive moving average processes, |

`x ~ arima()` | autoregressive integrated moving average processes, and |

`x ~ arfima()` | fractionally integrated ARMA processes. |

For the first selection `x~ar()`

the function `armaFit()`

uses the AR modelling algorithm as implemented in **R**'s `stats`

package.

For the second `x~ma()`

, third `x~arma()`

, and fourth
selection `x~arima()`

the function `armaFit()`

uses the
ARMA modelling algorithm also as implemented in **R**'s `stats`

package.

For the last selection `x~arfima()`

the function `armaFit()`

uses the fractional ARIMA modelling algorithm from **R**'s contributed
`fracdiff`

package.

Note, that the AR, MA, and ARMA processes can all be modelled by the
same algorithm specifying the formula `x~arima(p,d,q)`

in the
proper way, i.e. setting `d=0`

and choosing the orders of `p`

and `q`

as zero in agreement with the desired model specification.

Alternatively, one can still use the functions from R's `"stats"`

package: `arima.sim`

that simulates from an ARIMA time series
model, `ar, arima, arima0`

that fit an AR, ARIMA model to an
univariate time series, `predict`

that forecasts from a fitted
model, and `tsdiag`

that plots time-series diagnostics.
No function from these packages is masked, modified or overwritten.

The output of the `print`

, `summary`

, and `predict`

methods have all the same style of format for each time series
model with some additional algorithm specific printing. This makes
it easier to interpret the results obtained from different algorithms
implemented in different functions.

For `arfima`

models the following methods are not yet
implemented: `plot`

, `fitted`

, `residuals`

,
`predict`

, and `predictPlot`

.

M. Plummer and B.D. Ripley for `ar`

functions and code,

B.D. Ripley for `arima`

and `ARMAacf`

functions and code,

C. Fraley and F. Leisch for `fracdiff`

functions and code, and

Diethelm Wuertz for the Rmetrics **R**-port.

Brockwell, P.J. and Davis, R.A. (1996);
*Introduction to Time Series and Forecasting*,
Second Edition, Springer, New York.

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

Gardner, G, Harvey, A.C., Phillips, G.D.A. (1980);
*Algorithm AS154. An algorithm for exact maximum likelihood
estimation of autoregressive-moving average models by means of
Kalman filtering*,
Applied Statistics, 29, 311–322.

Hannan E.J. and Rissanen J. (1982);
*Recursive Estimation of Mixed Autoregressive-Moving
Average Order.*
Biometrika 69, 81–94.

Harvey, A.C. (1993);
*Time Series Models*,
2nd Edition, Harvester Wheatsheaf, Sections 3.3 and 4.4.

Jones, R.H. (1980);
*Maximum likelihood fitting of ARMA models to time
series with missing observations*,
Technometrics, 20, 389–395.

Percival, D.P. and Walden, A.T. (1998);
*Spectral Analysis for Physical Applications.*
Cambridge University Press.

Whittle, P. (1963);
*On the fitting of multivariate autoregressions
and the approximate canonical factorization of a spectral
matrix.*
Biometrika 40, 129–134.

Haslett J. and Raftery A.E. (1989);
*Space-time Modelling with Long-memory Dependence: Assessing
Ireland's Wind Power Resource (with Discussion)*,
Applied Statistics 38, 1–50.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | ```
## armaSim -
# Simulation:
x = armaSim(model = list(ar = c(0.5, -0.5), ma = 0.1), n = 1000)
## armaFit -
# Estimate the Parameters:
fit = armaFit(~ arma(2, 1), data = x)
print(fit)
## summary -
# Diagnostic Analysis:
par(mfrow = c(2, 2), cex = 0.7)
summary(fit, which = "all")
## plot -
# Interactive Plots:
# par(mfrow = c(1, 1))
# plot(fit)
## predict -
# Forecast 5 Steps Ahead:
par(mfrow = c(1, 1))
predict(fit, 5)
## armaFit -
# Alternative Calls:
TS = MSFT
armaFit(formula = diff(log(Close)) ~ ar(5), data = TS)
armaFit(Close ~ ar(5), data = returns(TS, digits = 12))
TS.RET = returns(TS, digits = 12)
armaFit(Close ~ ar(5), TS.RET)
armaFit(Close ~ ar(5), as.data.frame(TS.RET))
armaFit(~ ar(5), as.vector(TS.RET[, "Close"]))
armaFit(~ ar(5), as.ts(TS.RET)[, "Close"])
attach(TS.RET)
armaFit(Close ~ ar(5))
detach(TS.RET)
``` |

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