EEMDARIMA {eemdARIMA}

Ensemble Empirical Mode Decomposition Based ARIMA Model

Description

The EEMDARIMA function computes forecasted value with different forecasting evaluation criteria for Ensemble Empirical Mode Decomposition based ARIMA Model.

Usage

EEMDARIMA(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 mode mixing in EMD decomposition technique, Ensemble Empirical Mode Decomposition (EEMD) method was developed by Wu and Huang (2009). EEMD 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 used to measure the out of sample performance.

AllIMF_forecast

Forecasted value of all individual IMF.

FinalEEMDARIMA_forecast

Final forecasted value of the EEMD based ARIMA model. It is obtained by combining the forecasted value of all individual IMF.

MAE_EEMDARIMA

Mean Absolute Error (MAE) for EEMD based ARIMA model.

MAPE_EEMDARIMA

Mean Absolute Percentage Error (MAPE) for EEMD based ARIMA model.

rmse_EEMDARIMA

Root Mean Square Error (RMSE) for EEMD based ARIMA 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.

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

emdARIMA

Examples

Data("Data_Maize")
EEMDARIMA(Data_Maize)