emdLSTM {EEMDlstm}

Empirical Mode Decomposition (EEMD) Based Long Short Term (LSTM) Model

Description

The emdLSTM function computes forecasted value with different forecasting evaluation criteria for EMD based LSTM model.

Usage

emdLSTM(data, spl=0.8, num.IMFs=emd_num_imfs(length(data)),
s.num=4L, num.sift=50L,lag = 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.

lag

Lag of time series data.

LU

Number of unit in GRU layer.

Epochs

Number of epochs.

Details

A time series is decomposed by EMD into a set of intrinsic mode functions (IMFs) and a residual, which are modelled and predicted independently using LSTM 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.

FinalEEMDLSTM_forecast

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

MAE_EEMDLSTM

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

MAPE_EEMDLSTM

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

rmse_EEMDLSTM

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

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.

Jha, G.K. and Sinha, K. (2014) Time delay neural networks for time series prediction: An application to the monthly wholesale price of oilseeds in India. Neural Computing and Applications, 24, 563–571.

See Also

eemdLSTM

Examples

data("Data_Maize")
emdLSTM(Data_Maize)