CRAN/E | mrf

mrf

Multiresolution Forecasting

Installation

About

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.

www.deepbionics.org
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Key Metrics

Version 0.1.6
R ≥ 3.5.0
Published 2022-02-23 959 days ago
Needs compilation? no
License GPL-3
CRAN checks mrf results

Downloads

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Last 7 days 41 -45%
Last 30 days 251 -26%
Last 90 days 810 +2%
Last 365 days 3.239 +8%

Maintainer

Maintainer

Quirin Stier

Authors

Quirin Stier

aut / cre

ctr
Michael Thrun

ths / cph / rev / fnd / ctb

Material

Reference manual
Package source

In Views

TimeSeries

Vignettes

The mrf package

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

mrf archive

Depends

R ≥ 3.5.0

Imports

limSolve
DEoptim
stats
forecast
monmlp
nnfor

Suggests

knitr
rmarkdown