Type: | Package |
Title: | EEMD and Its Variant Based Time Delay Neural Network Model |
Version: | 0.1.0 |
Maintainer: | Kapil Choudhary <choudharykapil832@gmail.com> |
Description: | Forecasting univariate time series with different decomposition based time delay neural network models. For method details see Yu L, Wang S, Lai KK (2008). <doi:10.1016/j.eneco.2008.05.003>. |
License: | GPL-3 |
Encoding: | UTF-8 |
LazyData: | true |
RoxygenNote: | 7.1.1 |
Imports: | forecast, Rlibeemd |
Depends: | R (≥ 2.10) |
NeedsCompilation: | no |
Packaged: | 2021-01-15 13:53:56 UTC; Rajeev-PC |
Author: | Kapil Choudhary [aut, cre], Girish Kumar Jha [aut, ths, ctb], Rajeev Ranjan Kumar [aut, ctb], Ronit Jaiswal [ctb] |
Repository: | CRAN |
Date/Publication: | 2021-01-19 09:00:02 UTC |
Monthly International Maize Price Data
Description
Monthly international Maize price (Dollor per million ton) from January 2001 to December 2019.
Usage
data("Data_Maize")
Format
A time series data with 228 observations.
price
a time series
Details
Dataset contains 228 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)
Ensemble Empirical Mode Decomposition Based Time Delay Neural Network Model
Description
The EEMDTDNN function computes forecasted value with different forecasting evaluation criteria for Ensemble Empirical Mode Decomposition based Time Delay Neural Network Model.
Usage
EEMDTDNN(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. |
FinalEEMDTDNN_forecast |
Final forecasted value of the EEMD based TDNN model. It is obtained by combining the forecasted value of all individual IMF. |
MAE_EEMDTDNN |
Mean Absolute Error (MAE) for EEMD based TDNN model. |
MAPE_EEMDTDNN |
Mean Absolute Percentage Error (MAPE) for EEMD based TDNN model. |
rmse_EEMDTDNN |
Root Mean Square Error (RMSE) for EEMD based TDNN 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
emdTDNN, ceendanTDNN
Examples
Data("Data_Maize")
EEMDTDNN(Data_Maize)
CEEMDAN Based Time Delay Neural Network Model
Description
The ceemdanTDNN function computes forecasted value for Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise Based Time Delay Neural Network Model with different forecasting evaluation criteria.
Usage
ceemdanTDNN(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
Torres et al.(2011) proposed Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN). This algorithm generates a Fewer IMFs on the premise of successfully separating different components of a series, which can reduce the computational cost.
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 |
FinalCEEMDANTDNN_forecast |
Final forecasted value of the CEEMDAN based TDNN model. It is obtained by combining the forecasted value of all individual IMF. |
MAE_CEEMDANTDNN |
Mean Absolute Error (MAE) for CEEMDAN based TDNN model. |
MAPE_CEEMDANTDNN |
Mean Absolute Percentage Error (MAPE) for CEEMDAN based TDNN model. |
rmse_CEEMDANTDNN |
Root Mean Square Error (RMSE) for CEEMDAN based TDNN model. |
References
Torres, M.E., Colominas, M.A., Schlotthauer, G. and Flandrin, P. (2011) A complete ensemble empirical mode decomposition with adaptive noise. In 2011 IEEE international conference on acoustics, speech and signal processing (ICASSP) (pp. 4144–4147). IEEE.
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
emdTDNN, EEMDTDNN
Examples
data("Data_Maize")
ceemdanTDNN(Data_Maize)
Empirical Mode Decomposition Based Time Delay Neural Network Model
Description
The emdTDNN function gives forecasted value of Empirical Mode Decomposition based Time Delay Neural Network Model with different forecasting evaluation criteria.
Usage
emdTDNN(data, stepahead=10,
num.IMFs=emd_num_imfs(length(data)),
s.num=4L, num.sift=50L)
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. |
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, time delay neural network 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. |
FinalEMDTDNN_forecast |
Final forecasted value of the EMD based TDNN model. It is obtained by combining the forecasted value of all individual IMF. |
MAE_EMDTDNN |
Mean Absolute Error (MAE) for EMD based TDNN model. |
MAPE_EMDTDNN |
Mean Absolute Percentage Error (MAPE) for EMD based TDNN model. |
rmse_EMDTDNN |
Root Mean Square Error (RMSE) for EMD based TDNN 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.
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
EEMDTDNN, ceemdanTDNN
Examples
data("Data_Maize")
emdTDNN(Data_Maize)