multi_step: Multi Step Forecast
In Quirinms/MRFR: Multiresolution Forecasting

Description Usage Arguments Value Author(s) References Examples

This function creates a multi step forecast for all horizons from 1 to steps. The deployed forecast method can be an autoregression or a neural network (multilayer perceptron with one hidden layer). Multi step forecasts are computed recursively.

1	multi_step(UnivariateData, Horizon, CoefficientCombination, Aggregation, Method = "r")

`UnivariateData`	[1:n] Numerical vector with n values.
`Horizon`	Number indicating horizon for forecast from 1 to horizon.
`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.
`Method`	String indicating which method to use. Available methods: 'r' = Autoregression. 'nn' = Neural Network.

List of

multistep

[1:Horizon] Numerical vector with forecast of horizon according to its index.

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.

1 2 3	data(AirPassengers) len_data = length(array(AirPassengers)) multistep = multi_step(as.vector(AirPassengers)[1:(len_data-1)], 2, c(1,1,1), c(2,4), Method="r")