Introduction | The Ground Truth: Three Dynamic Latent Components | Visualize the Ground Truth | Method 1: DiPCA (Dynamic Inner PCA) | How Well Did DiPCA Recover the Components? | DiPCA Properties: Orthogonality | DiPCA Properties: Predictability | Method 2: DiCCA (Dynamic Inner Canonical Correlation) | DiCCA Property: Descending Predictability | Compare DiPCA and DiCCA | Reconstruction: Going Back to Original Space | Dynamic Whitening: Removing Autocorrelation | Summary | When to Use Which Method? | References | Session Info
Package: dipca 0.1.0
dipca: Dynamic Inner Principal Component Analysis
Methods for dynamic inner principal component analysis (DiPCA), dynamic inner canonical correlation analysis (DiCCA), and dynamic inner partial least squares (DiPLS) for multivariate time series. These methods extract latent components that follow autoregressive models, enabling temporal prediction and reconstruction. Based on Dong and Qin (2018) <doi:10.1016/j.jprocont.2017.05.002>, Dong and Qin (2018) <doi:10.1016/j.ifacol.2018.09.379>, and Dong and Qin (2018) <doi:10.1016/j.jprocont.2018.04.006>. Integrates with the 'multivarious' package for consistent preprocessing and projection interfaces.
Authors:
dipca_0.1.0.tar.gz
dipca_0.1.0.tgz(r-4.6-x86_64)dipca_0.1.0.tgz(r-4.6-arm64)dipca_0.1.0.tgz(r-4.5-x86_64)dipca_0.1.0.tgz(r-4.5-arm64)
dipca_0.1.0.tar.gz(r-4.7-arm64)dipca_0.1.0.tar.gz(r-4.7-x86_64)dipca_0.1.0.tar.gz(r-4.6-arm64)dipca_0.1.0.tar.gz(r-4.6-x86_64)
dipca_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
dipca/json (API)
| # Install 'dipca' in R: |
| install.packages('dipca', repos = c('https://bbuchsbaum.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/bbuchsbaum/dipca/issues
Last updated from:d82339a377. Checks:8 NOTE, 2 OK, 3 FAIL. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | NOTE | 177 | ||
| linux-devel-x86_64 | NOTE | 186 | ||
| source / vignettes | OK | 231 | ||
| linux-release-arm64 | NOTE | 178 | ||
| linux-release-x86_64 | NOTE | 230 | ||
| macos-release-arm64 | NOTE | 104 | ||
| macos-release-x86_64 | NOTE | 242 | ||
| macos-oldrel-arm64 | NOTE | 101 | ||
| macos-oldrel-x86_64 | NOTE | 241 | ||
| windows-devel | FAIL | 89 | ||
| windows-release | FAIL | 97 | ||
| windows-oldrel | FAIL | 88 | ||
| wasm-release | OK | 184 |
Exports:arima_diccadiccaDiCCAdicca_predict_scoresdicca_scoresdipcaDiPCAdipca_fitdiplsdipls_scores
Dependencies:chkclicolorspacecorpcorcpp11farverforecastfracdiffgeigengenericsggplot2gluegtableisobandlabelinglatticelifecyclelmtestmagrittrMatrixmultivariousnlmennetpillarpkgconfigR6RColorBrewerRcppRcppArmadillorlangS7scalestibbletimeDateurcautf8vctrsviridisLitewithrzoo
Extracting Dynamic Components from Multivariate Time Series
Last update: 2026-06-25
Started: 2025-10-21
Getting started with dipca
Why dipca? | Quick example | DiPCA: unsupervised dynamic components | DiCCA: components ordered by predictability | DiPLS: supervised dynamic components | Projecting new data | Next steps
Last update: 2026-06-25
Started: 2026-02-14
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Compact DiCCA with AR/ARMA/ARIMA inner models (measurement-space update) | arima_dicca |
| Dynamic-Inner Canonical Correlation Analysis (DiCCA) Extract dynamic latent variables by maximizing correlation between each latent series and its AR(s) prediction (canonical correlation form). This follows Dong & Qin (2018 IFAC) iteration and deflation. | DiCCA dicca |
| One-step-ahead prediction of DLVs (diagonal G(B)) | dicca_predict_scores |
| Transform new data to Di(L)V scores using a fitted compact DiCCA model | dicca_scores |
| Dynamic Inner PCA (DiPCA) | DiPCA dipca dipca_fit |
| Dynamic-inner Partial Least Squares (DiPLS) | dipls |
| Project X to DiPLS scores | dipls_scores |
| Predict Method for DiCCA | predict.dicca |
| Predict Method for DiPCA | predict.dipca |
| Predict Y using a fitted DiPLS model | predict.dipls |
| Residuals Method for DiCCA | residuals.dicca |
| Residuals Method for DiPCA | residuals.dipca |
| Compute residuals for DiPLS model | residuals.dipls |
