vmdRNN: Variational Mode Decomposition Based RNN Model

In decompDL: Decomposition Based Deep Learning Models for Time Series Forecasting

Variational Mode Decomposition Based RNN Model

Description

This function computes forecasted value with different forecasting evaluation criteria for Variational Mode Decomposition (VMD) Based RNN Model.

Usage

vmdRNN (data, spl=0.8, n=4, alpha=2000, tau=0, D=FALSE, LU = 2, Epochs = 2)

Arguments

data	Input univariate time series (ts) data.
spl	The forecast horizon.
n	The number of IMFs.
alpha	The balancing parameter.
tau	Time-step of the dual ascent.
D	a boolean.
LU	Number of unit in RNN layer.
Epochs	Number of epochs.

Details

The Variational Mode Decomposition method is a novel adaptive, non-recursive signal decomposition technology, which was introduced by Dragomiretskiy and Zosso (2014). VMD method helps to solve current decomposition methods limitation such as lacking mathematical theory, recursive sifting process which not allows for backward error correction, hard-band limits, the requirement to predetermine filter bank boundaries, and sensitivity to noise. It decomposes a series into sets of IMFs. RNN used to forecast decomposed components individually . Finally, the prediction results of all components are aggregated to formulate an ensemble output for the 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.
FinalVMDRNN_forecast	Final forecasted value of the VMD based RNN model. It is obtained by combining the forecasted value of all individual IMF.
MAE_VMDRNN	Mean Absolute Error (MAE) for VMD based RNN model.
MAPE_VMDRNN	Mean Absolute Percentage Error (MAPE) for VMD based RNN model.
rmse_VMDRNN	Root Mean Square Error (RMSE) for VMD 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

emdRNN

Examples

data("Data_Maize")
vmdRNN(Data_Maize)

decompDL documentation built on May 29, 2024, 4:45 a.m.