EEMDELM: Ensemble Empirical Mode Decomposition Based ELM Model

Description Usage Arguments Details Value References See Also Examples

View source: R/EEMDELM.R

Description

The EEMDelm function computes forecasted value with different forecasting evaluation criteria for Ensemble Empirical Mode Decomposition based Extreme Learning Machine model.

Usage

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EEMDELM(data, stepahead=10,
num.IMFs=emd_num_imfs(length(data)), s.num=4L,
num.sift=50L, ensem.size=250L, noise.st=0.2)

Arguments

data

Input univariate time series (ts) data.

stepahead

The forecast horizon.

num.IMFs

Number of Intrinsic Mode Function (IMF) for input series.

s.num

Integer. Use the S number stopping criterion for the EMD procedure with the given values of S. That is, iterate until the number of extrema and zero crossings in the signal differ at most by one, and stay the same for S consecutive iterations.

num.sift

Number of siftings to find out IMFs.

ensem.size

Number of copies of the input signal to use as the ensemble.

noise.st

Standard deviation of the Gaussian random numbers used as additional noise. This value is relative to the standard deviation of the input series.

Details

To overcome the problem of EMD (i.e. mode mixing), Ensemble Empirical Mode Decomposition (EEMD) method was developed by Wu and Huang (2009), which significantly reduces the chance of mode mixing and represents a substantial improvement over the original EMD.

Value

TotalIMF

Total number of IMFs.

AllIMF

List of all IMFs with residual for input series.

data_test

Testing set is used to measure the out of sample performance.

AllIMF_forecast

Forecasted value of all individual IMF.

FinalEEMDELM_forecast

Final forecasted value of the EEMDELM model. It is obtained by combining the forecasted value of all individual IMF.

MAE_EEMDELM

Mean Absolute Error (MAE) for EEMDELM model.

MAPE_EEMDELM

Mean Absolute Percentage Error (MAPE) for EEMDELM model.

rmse_EEMDELM

Root Mean Square Error (RMSE) for EEMDELM model.

References

Choudhary, K., Jha, G.K., Kumar, R.R. and Mishra, D.C. (2019) Agricultural commodity price analysis using ensemble empirical mode decomposition: A case study of daily potato price series. Indian journal of agricultural sciences, 89(5), 882–886.

Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q. and Liu, H.H. (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non stationary time series analysis. In Proceedings of the Royal Society of London A: mathematical, physical and engineering sciences. 454, 903–995.

Huang, G.B., Zhu, Q.Y. and Siew, C.K. (2006) Extreme learning machine: theory and applications. Neurocomputing, 70, 489–501.

Wu, Z. and Huang, N.E. (2009) Ensemble empirical mode decomposition: a noise assisted data analysis method. Advances in adaptive data analysis, 1(1), 1–41.

See Also

EMDelm, CEEMDANelm

Examples

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data("Data_Soybean")
EEMDELM(Data_Soybean)

EEMDelm documentation built on Jan. 15, 2021, 3:41 p.m.

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