% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/tsAirgapComplete.R
\docType{data}
\name{tsAirgapComplete}
\alias{tsAirgapComplete}
\title{Time series of monthly airline passengers (complete)}
\format{
Time Series (\code{\link{ts}}) with 144 rows.
}
\source{
\cite{Box, G. E. P., Jenkins, G. M., Reinsel, G. C. and Ljung, G. M. (2015). Time series analysis: forecasting and control. Fifth Edition. John Wiley and Sons.}
}
\usage{
tsAirgapComplete
}
\description{
Monthly totals of international airline passengers, 1949 to 1960.
This time series provides the truth for the missing values of the \code{\link{tsAirgap}} time series. Thus it is identical
to the tsAirgap time series except that no value is missing.
}
\details{
The dataset originates from Box and Jenkins (see citation) and is a commonly used example in
time series analysis literature.

It characteristics (strong trend, strong seasonal behavior) make it also a great
example for time series imputation.
Thus the version with inserted NA gaps was created under the name tsAirgap.

In order to use this series for comparing imputation algorithm results,
there are two time series provided. One series without missing values, which can
be used as ground truth. Another series with NAs, on which the imputation
algorithms can be applied.

There are the two time series:
\itemize{
\item tsAirgap - The time series with NAs.

\item tsAirgapComplete - Time series without NAs.
}
}
\seealso{
\code{\link[imputeTS]{tsHeating}}, \code{\link[imputeTS]{tsNH4}}
}
\keyword{datasets}
