neuralnet_one_step: One Step Forecast with Neural Network
In Quirinms/MRFR: Multiresolution Forecasting

Description Usage Arguments Value Author(s) References Examples

This function creates a one step forecast using a multi layer perceptron with one hidden Layer. The number of input is the sum of all coefficients chosen with the parameter CoefficientCombination. The CoefficientCombination parameter controls the number of coefficients chosen for each wavelet and smooth part level individually.

1	neuralnet_one_step(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.

forecast

Numerical value with one step forecast

Quirin Stier

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.

data(AirPassengers)
len_data = length(array(AirPassengers))
forecast = neuralnet_one_step(as.vector(AirPassengers)[1:(len_data-1)], c(1,1,1), c(2,4))
true_value = array(AirPassengers)[len_data]
error = true_value - forecast