Description Usage Arguments Value Author(s) References Examples
View source: R/mrf_requirement.R
Computes requirements for given model using insights of various published papers and the manuscript [Stier et al., 2021] which is currently in press.
1 | mrf_requirement(UnivariateData, CoefficientCombination, Aggregation)
|
UnivariateData |
[1:n] Numerical vector with n values. |
CoefficientCombination |
[1:Scales+1] Numerical vector with numbers which are associated with wavelet levels. The last number is associated with the smooth level. Each number determines the number of coefficient used per level. The selection follows a specific scheme. |
Aggregation |
[1:Scales] Numerical vector carrying numbers whose index is associated with the wavelet level. The numbers indicate the number of time in points used for aggregation from the original time series. |
List of
MinLen |
Integer minimum required length for model. |
StartTraining |
Integer indicating the index of time series at which the training equations can be built up. |
NumberWeights |
Number of weights required for building model. |
NumberEquations |
Number of equations which can be built with given data. |
Quirin Stier
[Stier et al., 2021] Stier, Q.,Gehlert, T. and Thrun, M. C.: Multiresolution Forecasting for Industrial Applications, Processess, 2021.
Aussem, A., Campbell, J., and Murtagh, F. Waveletbased Feature Extraction and Decomposition Strategies for Financial Forecasting. International Journal of Computational Intelligence in Finance, 6,5-12, 1998.
Renaud, O., Starck, J.-L., and Murtagh, F. Prediction based on a Multiscale De- composition. International Journal of Wavelets, Multiresolution and Information Processing, 1(2):217-232. doi:10.1142/S0219691303000153, 2003.
Murtagh, F., Starck, J.-L., and Renaud, O. On Neuro-Wavelet Modeling. Decision Support Systems, 37(4):475-484. doi:10.1016/S0167-9236(03)00092-7, 2004.
Renaud, O., Starck, J.-L., and Murtagh, F. Wavelet-based combined Signal Filter- ing and Prediction. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 35(6):1241-1251. doi:10.1109/TSMCB.2005.850182, 2005.
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