The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

mrf: Multiresolution Forecasting

Forecasting of univariate time series using feature extraction with variable prediction methods is provided. Feature extraction is done with a redundant Haar wavelet transform with filter h = (0.5, 0.5). The advantage of the approach compared to typical Fourier based methods is an dynamic adaptation to varying seasonalities. Currently implemented prediction methods based on the selected wavelets levels and scales are a regression and a multi-layer perceptron. Forecasts can be computed for horizon 1 or higher. Model selection is performed with an evolutionary optimization. Selection criteria are currently the AIC criterion, the Mean Absolute Error or the Mean Root Error. The data is split into three parts for model selection: Training, test, and evaluation dataset. The training data is for computing the weights of a parameter set. The test data is for choosing the best parameter set. The evaluation data is for assessing the forecast performance of the best parameter set on new data unknown to the model. This work is published in Stier, Q.; Gehlert, T.; Thrun, M.C. Multiresolution Forecasting for Industrial Applications. Processes 2021, 9, 1697. <doi:10.3390/pr9101697>.

Version: 0.1.6
Depends: R (≥ 3.5.0)
Imports: limSolve, DEoptim, stats, forecast, monmlp, nnfor
Suggests: knitr, rmarkdown
Published: 2022-02-23
DOI: 10.32614/CRAN.package.mrf
Author: Quirin Stier [aut, cre, ctr], Michael Thrun ORCID iD [ths, cph, rev, fnd, ctb]
Maintainer: Quirin Stier <research at quirin-stier.de>
BugReports: https://github.com/Quirinms/MRFR/issues
License: GPL-3
URL: https://www.deepbionics.org
NeedsCompilation: no
In views: TimeSeries
CRAN checks: mrf results

Documentation:

Reference manual: mrf.pdf
Vignettes: The mrf package

Downloads:

Package source: mrf_0.1.6.tar.gz
Windows binaries: r-devel: mrf_0.1.6.zip, r-release: mrf_0.1.6.zip, r-oldrel: mrf_0.1.6.zip
macOS binaries: r-release (arm64): mrf_0.1.6.tgz, r-oldrel (arm64): mrf_0.1.6.tgz, r-release (x86_64): mrf_0.1.6.tgz, r-oldrel (x86_64): mrf_0.1.6.tgz
Old sources: mrf archive

Linking:

Please use the canonical form https://CRAN.R-project.org/package=mrf to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.