| c_binarization {otsfeatures} | R Documentation |
Constructs the cumulative binarized time series associated with a given ordinal time series
Description
c_binarization constructs the cumulative binarized time series associated with a given
ordinal time series.
Usage
c_binarization(series, states)
Arguments
series |
An OTS (numerical vector with integers). |
states |
A numeric vector containing the corresponding states. |
Details
Given an OTS of length T with range \mathcal{S}=\{s_0, s_1, s_2, \ldots, s_n\} (s_0 < s_1 < s_2 < \ldots < s_n),
\overline{X}_t=\{\overline{X}_1,\ldots, \overline{X}_T\}, the function
constructs the cumulative binarized time series, which is defined as
\overline{\boldsymbol Y}_t=\{\overline{\boldsymbol Y}_1, \ldots, \overline{\boldsymbol Y}_T\},
with \overline{\boldsymbol Y}_k=(\overline{Y}_{k,0}, \overline{Y}_{k,1},\ldots, \overline{Y}_{k,n-1})^\top
such that \overline{Y}_{k,i}=1 if \overline{X}_k \le s_i (k=1,\ldots,T,
, i=0,\ldots,n-1). The cumulative binarized series is constructed in the form of a matrix
whose rows represent time observations and whose columns represent the
states in the original series.
Value
The binarized time series.
Author(s)
Ángel López-Oriona, José A. Vilar
References
Weiß CH (2018). An introduction to discrete-valued time series. John Wiley and Sons.
López-Oriona Á, Vilar JA, D’Urso P (2023). “Hard and soft clustering of categorical time series based on two novel distances with an application to biological sequences.” Information Sciences, 624, 467–492.
Examples
c_binarized_series <- c_binarization(AustrianWages$data[[100]],
states = 0 : 5) # Constructing the cumulative binarized
# time series for one OTS in dataset AustrianWages