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# CITATION file created with {cffr} R package
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cff-version: 1.2.0
message: 'To cite package "mrf" in publications use:'
type: software
license: GPL-3.0-only
title: 'mrf: Multiresolution Forecasting'
version: 0.1.6
doi: 10.32614/CRAN.package.mrf
abstract: '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. <https://doi.org/10.3390/pr9101697>.'
authors:
- family-names: Stier
  given-names: Quirin
  email: research@quirin-stier.de
repository: https://quirinms.r-universe.dev
repository-code: https://github.com/Quirinms/MRFR
commit: c84a61e9db8d59862553e4260cd2fa673c48ab52
url: https://www.deepbionics.org
date-released: '2021-09-20'
contact:
- family-names: Stier
  given-names: Quirin
  email: research@quirin-stier.de