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otsfeatures (version 1.0.0)

Ordinal Time Series Analysis

Description

An implementation of several functions for feature extraction in ordinal time series datasets. Specifically, some of the features proposed by Weiss (2019) 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'.

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Version

Install

install.packages('otsfeatures')

Monthly Downloads

212

Version

1.0.0

License

GPL-2

Maintainer

Angel Lopez-Oriona

Last Published

March 1st, 2023

Functions in otsfeatures (1.0.0)

ordinal_cohens_kappa

Computes the estimated ordinal Cohen's kappa of an ordinal time series
marginal_probabilities

Computes the marginal probabilities of an ordinal time series
ordinal_dispersion_1

Computes the standard estimated dispersion of an ordinal time series
ordinal_asymmetry

Computes the estimated asymmetry of an ordinal time series
conditional_probabilities

Computes the conditional probabilities of an ordinal time series
ci_ordinal_asymmetry

Constructs a confidence interval for the ordinal asymmetry (block distance)
ci_ordinal_dispersion

Constructs a confidence interval for the ordinal dispersion (block distance)
index_ordinal_variation

Computes the estimated index of ordinal variation (IOV) of an ordinal time series
ci_ordinal_skewness

Constructs a confidence interval for the ordinal skewness (block distance)
joint_probabilities

Computes the joint probabilities of an ordinal time series
test_ordinal_dispersion

Performs the hypothesis test associated with the ordinal dispersion for the block distance
test_ordinal_asymmetry

Performs the hypothesis test associated with the ordinal asymmetry for the block distance
ots_plot

Constructs an ordinal time series plot
plot_ordinal_cohens_kappa

Constructs a serial dependence plot based on the ordinal Cohen's kappa considering the block distance
total_c_correlation

Computes the total cumulative correlation of an ordinal time series
test_ordinal_skewness

Performs the hypothesis test associated with the ordinal skewness for the block distance
ordinal_location_2

Computes the estimated location of an ordinal time series with respect to the lowest category
total_mixed_c_correlation_1

Computes the total mixed cumulative linear correlation (TMCLC) between an ordinal and a real-valued time series
total_mixed_c_correlation_2

Computes the total mixed cumulative quantile correlation (TMCQC) between an ordinal and a real-valued time series
ordinal_skewness

Computes the estimated skewness of an ordinal time series
ordinal_location_1

Computes the standard estimated location of an ordinal time series
ordinal_dispersion_2

Computes the estimated dispersion of an ordinal time series according to the approach based on the diversity coefficient (DIVC)
c_binarization

Constructs the cumulative binarized time series associated with a given ordinal time series
c_joint_probabilities

Computes the cumulative joint probabilities of an ordinal time series
AustrianWages

AustrianWages
SyntheticData3

SyntheticData3
c_marginal_probabilities

Computes the cumulative marginal probabilities of an ordinal time series
binarization

Constructs the binarized time series associated with a given ordinal time series
c_conditional_probabilities

Computes the cumulative conditional probabilities of an ordinal time series
CreditRatings

CreditRatings
SyntheticData2

SyntheticData2
SyntheticData1

SyntheticData1