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:
ForeCA_0.2.7.tar.gz
ForeCA_0.2.7.zip(r-4.5)ForeCA_0.2.7.zip(r-4.4)ForeCA_0.2.7.zip(r-4.3)
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)
NEWS
| # Install 'ForeCA' in R: |
| install.packages('ForeCA', repos = c('https://gmgeorg.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/gmgeorg/foreca/issues
blind-source-separationdimensionality-reductionforecastingmultivariate-timeseriessignal-processingspectrumtime-seriestime-series-analysis
Last updated 4 years agofrom:d6ff736c37. Checks:OK: 3 NOTE: 4. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 13 2024 |
| R-4.5-win | NOTE | Nov 13 2024 |
| R-4.5-linux | NOTE | Nov 13 2024 |
| R-4.4-win | NOTE | Nov 13 2024 |
| R-4.4-mac | NOTE | Nov 13 2024 |
| R-4.3-win | OK | Nov 13 2024 |
| R-4.3-mac | OK | Nov 13 2024 |
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
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Implementation of Forecastable Component Analysis (ForeCA) | ForeCA-package ForeCA |
| List of common arguments | common-arguments |
| Completes several control settings | complete-controls complete_algorithm_control complete_entropy_control complete_spectrum_control |
| Shannon entropy for a continuous pdf | continuous_entropy |
| Shannon entropy for discrete pmf | discrete_entropy |
| Forecastable Component Analysis | foreca foreca.multiple_weightvectors foreca.one_weightvector |
| Plot, summary, and print methods for class 'foreca' | biplot.foreca foreca-utils plot.foreca print.foreca summary.foreca |
| ForeCA EM auxiliary functions | foreca.EM-aux foreca.EM.E_and_M_step foreca.EM.E_step foreca.EM.h foreca.EM.M_step |
| EM-like algorithm to estimate optimal ForeCA transformation | foreca.EM.one_weightvector |
| Plot, summary, and print methods for class 'foreca.one_weightvector' | foreca.one_weightvector-utils plot.foreca.one_weightvector summary.foreca.one_weightvector |
| Initialize weightvector for iterative ForeCA algorithms | initialize_weightvector |
| Estimates spectrum of multivariate time series | check_mvspectrum_normalized get_spectrum_from_mvspectrum mvpgram mvspectrum normalize_mvspectrum spectrum_of_linear_combination |
| S3 methods for class 'mvspectrum' | mvspectrum-utils plot.mvspectrum |
| Compute (weighted) covariance matrix from frequency spectrum | mvspectrum2wcov weightvector2entropy_wcov |
| Estimate forecastability of a time series | Omega |
| Computes quadratic form x' A x | fill_hermitian quadratic_form |
| Slow Feature Analysis | sfa |
| Estimates spectral entropy of a time series | spectral_entropy |
| whitens multivariate data | check_whitened sqrt_matrix whiten |
