astsa (version 2.1)

sarima: Fit ARIMA Models

Description

Fits ARIMA models (with diagnostics) in a short command. It can also be used to perform regression with autocorrelated errors.

Usage

sarima(xdata, p, d, q, P = 0, D = 0, Q = 0, S = -1, 
       details = TRUE, xreg = NULL, Model = TRUE,
       fixed = NULL, tol = sqrt(.Machine$double.eps), 
       no.constant = FALSE, ...)

Value

A t-table, the estimated noise variance, and AIC, AICc, BIC are printed. The following are returned invisibly:

fit

the arima object

sigma2

the estimate of the noise variance

degrees_of_freedom

error degrees of freedom

ttable

a little t-table with two-sided p-values

ICs

AIC - AICc - BIC

Arguments

xdata

univariate time series

p

AR order (must be specified)

d

difference order (must be specified)

q

MA order (must be specified)

P

SAR order; use only for seasonal models

D

seasonal difference; use only for seasonal models

Q

SMA order; use only for seasonal models

S

seasonal period; use only for seasonal models

details

if FALSE, turns off the diagnostic plot and the output from the nonlinear optimization routine, which is optim. The default is TRUE.

xreg

Optionally, a vector or matrix of external regressors, which must have the same number of rows as xdata.

Model

if TRUE (default), the model orders are printed on the diagnostic plot.

fixed

optional numeric vector of the same length as the total number of parameters. If supplied, only parameters corresponding to NA entries will be estimated.

tol

controls the relative tolerance (reltol in optim) used to assess convergence. The default is sqrt(.Machine$double.eps), the R default.

no.constant

controls whether or not sarima includes a constant in the model. In particular, if there is no differencing (d = 0 and D = 0) you get the mean estimate. If there is differencing of order one (either d = 1 or D = 1, but not both), a constant term is included in the model. These two conditions may be overridden (i.e., no constant will be included in the model) by setting this to TRUE; e.g., sarima(x,1,1,0,no.constant=TRUE). Otherwise, no constant or mean term is included in the model. If regressors are included (via xreg), this is ignored.

...

additional graphical arguments

Details

If your time series is in x and you want to fit an ARIMA(p,d,q) model to the data, the basic call is sarima(x,p,d,q). The values p,d,q, must be specified as there is no default. The results are the parameter estimates, standard errors, AIC, AICc, BIC (as defined in Chapter 2) and diagnostics. To fit a seasonal ARIMA model, the basic call is sarima(x,p,d,q,P,D,Q,S). For example, sarima(x,2,1,0) will fit an ARIMA(2,1,0) model to the series in x, and sarima(x,2,1,0,0,1,1,12) will fit a seasonal ARIMA\((2,1,0)*(0,1,1)_{12}\) model to the series in x. The difference between the information criteria given by sarima() and arima() is that they differ by a scaling factor of the effective sample size.

References

You can find demonstrations of astsa capabilities at FUN WITH ASTSA.

The most recent version of the package can be found at https://github.com/nickpoison/astsa/.

In addition, the News and ChangeLog files are at https://github.com/nickpoison/astsa/blob/master/NEWS.md.

The webpages for the texts and some help on using R for time series analysis can be found at https://nickpoison.github.io/.

See Also

sarima.for, sarima.sim

Examples

# easy to use 
sarima(rec, 2,0,0)  # data, p, d, and q
sarima(rec, 2,0,0, details=FALSE)  # minimal output

dog <- sarima(log(AirPassengers), 0,1,1, 0,1,1,12)
str(dog, vec.len=1) # dog has all the returned values
tsplot(resid(dog$fit))  # plot the innovations (residuals) 
dog$ICs # view the 3 ICs

# fixed parameters
x = sarima.sim( ar=c(0,-.9), n=200 ) + 50 
sarima(x, 2,0,0, fixed=c(0,NA,NA))  # phi1 fixed, phi2 and mean free

# fun with diagnostics
sarima(log(AirPassengers), 0,1,1, 0,1,1,12, gg=TRUE, col=4)

# regression with autocorrelated errors 
pp = ts.intersect(L = Lynx, L1 = lag(Lynx,-1), H1 = lag(Hare,-1), dframe=TRUE)
sarima(pp$L, 2,0,0, xreg = cbind(L1=pp$L1, LH1=pp$L1*pp$H1))