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, in press, Processes 2021.


News

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("mrf")

0.1.5 by Quirin Stier, a month ago


https://www.deepbionics.org


Report a bug at https://github.com/Quirinms/MRFR/issues


Browse source code at https://github.com/cran/mrf


Authors: Quirin Stier [aut, cre, ctr] , Michael Thrun [ths, cph, rev, fnd, ctb]


Documentation:   PDF Manual  


Task views: Time Series Analysis


GPL-3 license


Imports limSolve, DEoptim, stats, forecast, monmlp, nnfor

Suggests knitr, rmarkdown


See at CRAN