The vector X of length \(T\) is broken into
Mcols blocks, each part containing \(T/\code{Mcols}\) elements.
If the vector X represents consecutive daily values, e.g., then
Mcols = 365 is preferred. This function rearranges X into a
matrix format, where each column is the vector of values at the same day
of the year. If it were monthly data it would be preferred, e.g.,
Mcols = 12.
series_split(X, Mcols = 365)A numeric vector.
An integer number, giving the number of columns in the final matrix.
A matrix with Mcols columns.
This function is used in the data preparation (or pre-processing) often required to apply the exploratory and inference tools based on theory of records within this package when the time series presents seasonality.
This function transforms a vector into a matrix, applying the
following procedure: the first row of the matrix is built of the first
Mcols elements of the vector, the second row by the Mcols
following elements, and so on.
The length of the vector must be a multiple of Mcols
(see Note otherwise).
In the case of a vector of daily values, Mcols is usually 365,
so that the first column corresponds to all the values observed at the
1st of January, the second to the 2nd of January, etc.
If \(X_{t,m}\) represents the value in day \(m\) of year \(t\), then if
$$\code{X} = (X_{1,1},X_{1,2},\ldots,X_{1,365},X_{2,1},X_{2,2},\ldots,X_{T,365}),$$
applying series_split to X returns the following matrix:
$$
\left( \begin{array}{cccc}
X_{1,1} & X_{1,2} & \cdots & X_{1,365} \\
X_{2,1} & X_{2,2} & \cdots & X_{2,365} \\
\vdots & \vdots & & \vdots \\
X_{T,1} & X_{T,2} & \cdots & X_{T,365}
\end{array} \right)_{T \times 365}.
$$
Cebri<U+00E1>n A, Castillo-Mateo J, As<U+00ED>n J (2021). <U+201C>Record Tests to detect non stationarity in the tails with an application to climate change.<U+201D> Unpublished manuscript.
series_double, series_record,
series_rev, series_ties,
series_uncor, series_untie
# NOT RUN {
series_split(1:100, Mcols = 10)
TxZ <- series_split(TX_Zaragoza$TX)
dim(TxZ)
# }
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