eemdARIMA: Ensemble Empirical Mode Decomposition Based Auto Regressive...

In decompML: Decomposition Based Machine Learning Model

Ensemble Empirical Mode Decomposition Based Auto Regressive Integrated Moving Average Model

Description

The eemdARIMA function gives forecasted value of Ensemble Empirical Mode Decomposition based Auto Regressive Integrated Moving Average Model with different forecasting evaluation criteria.

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 eemd 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

This function firstly, decompose the nonlinear and nonstationary time series into several independent intrinsic mode functions (IMFs) and one residual component (Huang et al., 1998). Secondly, Auto Regressive Integrated Moving Average is used to forecast these IMFs and residual component individually. Finally, the prediction results of all IMFs including residual are aggregated to form the final forecasted value for given input time series.

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 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 Ensemble 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.

Jha, G.K. and Sinha, K. (2014) Auto Regressive Integrated Moving Averages for time series prediction: An application to the monthly wholesale price of oilseeds in India. Neural Computing and Applications, 24, 563–571.

See Also

eemdARIMA, ceeemdanARIMA

Examples

data("Data_Maize")
eemdARIMA(Data_Maize)

decompML documentation built on April 3, 2025, 7:26 p.m.