Package: ForeCA 0.2.7

ForeCA: Forecastable Component Analysis

Implementation of Forecastable Component Analysis ('ForeCA'), including main algorithms and auxiliary function (summary, plotting, etc.) to apply 'ForeCA' to multivariate time series data. 'ForeCA' is a novel dimension reduction (DR) technique for temporally dependent signals. Contrary to other popular DR methods, such as 'PCA' or 'ICA', 'ForeCA' takes time dependency explicitly into account and searches for the most ''forecastable'' signal. The measure of forecastability is based on the Shannon entropy of the spectral density of the transformed signal.

Authors:Georg M. Goerg [aut, cre]

ForeCA_0.2.7.tar.gz
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ForeCA_0.2.7.tgz(r-4.4-any)ForeCA_0.2.7.tgz(r-4.3-any)
ForeCA_0.2.7.tar.gz(r-4.5-noble)ForeCA_0.2.7.tar.gz(r-4.4-noble)
ForeCA_0.2.7.tgz(r-4.4-emscripten)ForeCA_0.2.7.tgz(r-4.3-emscripten)
ForeCA.pdf |ForeCA.html✨
ForeCA/json (API)
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Peer review:

Bug tracker:https://github.com/gmgeorg/foreca/issues

On CRAN:

blind-source-separationdimensionality-reductionforecastingmultivariate-timeseriessignal-processingspectrumtime-seriestime-series-analysis

30 exports 13 stars 1.66 score 13 dependencies 39 scripts 408 downloads

Last updated 4 years agofrom:d6ff736c37. Checks:OK: 3 NOTE: 4. Indexed: yes.

Exports:check_mvspectrum_normalizedcheck_whitenedcomplete_algorithm_controlcomplete_entropy_controlcomplete_spectrum_controlcontinuous_entropydiscrete_entropyfill_hermitianforecaforeca.EM.E_and_M_stepforeca.EM.E_stepforeca.EM.hforeca.EM.M_stepforeca.EM.one_weightvectorforeca.multiple_weightvectorsforeca.one_weightvectorget_spectrum_from_mvspectruminitialize_weightvectormvpgrammvspectrummvspectrum2wcovnormalize_mvspectrumOmegaquadratic_formsfaspectral_entropyspectrum_of_linear_combinationsqrt_matrixweightvector2entropy_wcovwhiten

Dependencies:astsacligluelifecyclemagrittrMASSplyrRcppreshape2rlangstringistringrvctrs