Ordinal Time Series Analysis
Constructs the binarized time series associated with a given ordinal t...
Constructs the cumulative binarized time series associated with a give...
Computes the cumulative conditional probabilities of an ordinal time s...
Computes the cumulative joint probabilities of an ordinal time series
Computes the cumulative marginal probabilities of an ordinal time seri...
Constructs a confidence interval for the ordinal asymmetry (block dist...
Constructs a confidence interval for the ordinal dispersion (block dis...
Constructs a confidence interval for the ordinal skewness (block dista...
Computes the conditional probabilities of an ordinal time series
Computes the estimated index of ordinal variation (IOV) of an ordinal ...
Computes the joint probabilities of an ordinal time series
Computes the marginal probabilities of an ordinal time series
Computes the estimated asymmetry of an ordinal time series
Computes the estimated ordinal Cohen's kappa of an ordinal time series
Computes the standard estimated dispersion of an ordinal time series
Computes the estimated dispersion of an ordinal time series according ...
Computes the standard estimated location of an ordinal time series
Computes the estimated location of an ordinal time series with respect...
Computes the estimated skewness of an ordinal time series
Constructs an ordinal time series plot
Constructs a serial dependence plot based on the ordinal Cohen's kappa...
Performs the hypothesis test associated with the ordinal asymmetry for...
Performs the hypothesis test associated with the ordinal dispersion fo...
Performs the hypothesis test associated with the ordinal skewness for ...
Computes the total cumulative correlation of an ordinal time series
Computes the total mixed cumulative linear correlation (TMCLC) between...
Computes the total mixed cumulative quantile correlation (TMCQC) betwe...
An implementation of several functions for feature extraction in ordinal time series datasets. Specifically, some of the features proposed by Weiss (2019) <doi:10.1080/01621459.2019.1604370> can be computed. These features can be used to perform inferential tasks or to feed machine learning algorithms for ordinal time series, among others. The package also includes some interesting datasets containing financial time series. Practitioners from a broad variety of fields could benefit from the general framework provided by 'otsfeatures'.