Package 'EEMDlstm'

Monthly International Maize Price Data

Description

Monthly international Maize price (Dollor per million ton) from January 2010 to June 2020.

Usage

data("Data_Maize")

Format

A time series data with 126 observations.

price

a time series

Details

Dataset contains 126 observations of monthly international Maize price (Dollor per million ton). It is obtained from World Bank "Pink sheet".

Source

https://www.worldbank.org/en/research/commodity-markets

References

https://www.worldbank.org/en/research/commodity-markets

Examples

data(Data_Maize)

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

Description

The eemdLSTM function computes forecasted value with different forecasting evaluation criteria for EEMD based LSTM model.

Usage

eemdLSTM(data, spl=0.8, num.IMFs=emd_num_imfs(length(data)),
s.num=4L, num.sift=50L, ensem.size=250L, noise.st=0.2,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.
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.
lag	Lag of time series data.
LU	Number of unit in GRU layer.
Epochs	Number of epochs.

Details

A time series is decomposed by EEMD 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. EEMD overcomes the limitation of the mode mixing and end effect problems of the empirical mode decomposition (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.
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.

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

emdLSTM

Examples

data("Data_Maize")
eemdLSTM(Data_Maize)

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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)

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