marimapred: Multiple time series automatic ARIMA fitting and prediction
In TSPred: Functions for Benchmarking Time Series Prediction

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

The function predicts and returns the next n consecutive values of a set of time series using automatically fitted ARIMA models. Based on multiple application of the arimapred function.

marimapred(
  TimeSeries,
  TimeSeriesCont = NULL,
  n.ahead = NULL,
  na.action = stats::na.omit,
  xreg = NULL,
  newxreg = NULL,
  se.fit = FALSE,
  plot = FALSE,
  range.p = 0.2,
  ylab = NULL,
  xlab = NULL,
  main = NULL
)

`TimeSeries`	A vector, matrix, or data frame which contains a set of time series used for fitting ARIMA models. Each column corresponds to one time series.
`TimeSeriesCont`	A vector, matrix, or data frame containing continuation points for `TimeSeries` with actual values. Each column corresponds to one time series. Ignored if `NULL`.
`n.ahead`	A numeric vector (or a single numeric value) with the number of consecutive values which are to be predicted of each respective time series in `TimeSeries`. If `n.ahead` is `NULL`, the number of values to be predicted of each time series in `TimeSeries` is assumed to be equal to the number of rows in each respective time series in `TimeSeriesCont`. Required when `TimeSeriesCont` is `NULL`.
`na.action`	A function for treating missing values in `TimeSeries` and `TimeSeriesCont`. The default function is `na.omit`, which omits any missing values found in `TimeSeries` or `TimeSeriesCont`.
`xreg`	A list of vectors, matrices, data frames or times series of external regressors used for fitting the ARIMA models. The first component of the list contains external regressors for the first time series in `TimeSeries` and therefore must have the same number of rows as this respective time series. This is also valid for the second component, and so forth. Ignored if `NULL`.
`newxreg`	A list of vectors, matrices, data frames or times series with new values of `xreg` to be used for prediction. The first component of the list must have at least the same number of rows as the respective first value in `n.ahead` or, if `n.ahead` is `NULL`, the number of continuation points in the respective first time series in `TimeSeriesCont`. This is also valid for the second component, and so forth. Ignored if `NULL`.
`se.fit`	If `se.fit` is `TRUE`, the standard errors of the predictions are returned.
`plot`	A Boolean parameter which defines whether the function `arimapred` will generate a graphic. If `plot` is `TRUE`, graphics will be generated for each time series in `TimeSeries`.
`range.p`	A percentage which defines how much the range of the graphics' y-axis will be increased from the minimum limits imposed by data.
`ylab`	A title for the graphics' y-axis. Ignored if `NULL`. ~~Describe `ylab` here~~
`xlab`	A title for the graphics' x-axis. Ignored if `NULL`. ~~Describe `xlab` here~~
`main`	An overall title for the graphics. Ignored if `NULL`. ~~Describe `main` here~~

See the arimapred function.

A vector of time series of predictions, if the number of consecutive values predicted of each time series in TimeSeries is the same, otherwise a list of time series of predictions.

If se.fit is TRUE, a vector of lists, each one with the components pred, the predictions, and se, the estimated standard errors. Both components are time series. See the predict.Arima function in the stats package and the function arimapred.

Rebecca Pontes Salles

See the arimapred function. the literature/web site here ~

arimapred ~