ceemdRNN {decompDL}

Complementary Ensemble Empirical Mode Decomposition (CEEMD) Based Long Short Term (RNN) Model

Description

The eemdRNN function computes forecasted value with different forecasting evaluation criteria for EEMD based RNN model.

Usage

ceemdRNN(data, spl=0.8, num.IMFs=emd_num_imfs(length(data)),
s.num=4L, num.sift=50L, ensem.size=250L, noise.st=0.2,lg = 4,
LU = 2, Epochs = 2)

Arguments

data

Input univariate time series (ts) data.

spl

Index of the split point and separates the data into the training and testing datasets.

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.

lg

Lag of time series data.

LU

Number of unit in RNN layer.

Epochs

Number of epochs.

Details

A time series is decomposed by CEEMD into a set of intrinsic mode functions (IMFs) and a residual, which are modelled and predicted independently using RNN models. Finally, the ensemble output for the price series is produced by combining the forecasts of all IMFs and residuals.

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.

FinalCEEMDRNN_forecast

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

MAE_CEEMDRNN

Mean Absolute Error (MAE) for CEEMD based RNN model.

MAPE_CEEMDRNN

Mean Absolute Percentage Error (MAPE) for CEEMD based RNN model.

rmse_CEEMDRNN

Root Mean Square Error (RMSE) for CEEMD based RNN model.

AllIMF_plots

Decomposed IMFs and residual plot.

plot_testset

Test set forecasted vs actual value plot.

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

eemdRNN

Examples

data("Data_Maize")
ceemdRNN(Data_Maize)