VMDARIMA {vmdTDNN}

Variational Mode Decomposition Based Autoregressive Integrated Moving Average Model

Description

The VMDARIMA function computes forecasted value with different forecasting evaluation criteria for Variational Mode Decomposition (VMD) Based Autoregressive Integrated Moving Average (ARIMA).

Usage

VMDARIMA (data, stepahead=10, nIMF=4, alpha=2000, tau=0, D=FALSE)

Arguments

data

Input univariate time series (ts) data.

stepahead

The forecast horizon.

nIMF

The number of IMFs.

alpha

The balancing parameter.

tau

Time-step of the dual ascent.

D

a boolean.

Details

In this function, the variational mode decomposition (VMD) used for mining the trend features and detailed features contained in a time series. Moreover, the corresponding autoregressive integrated moving average (ARIMA) models were derived to reflect the different features of the IMFs. The final forecasted values obtained for a given time series.

Value

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

FinalVMDARIMA_forecast

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

MAE_VMDARIMA

Mean Absolute Error (MAE) for VMDARIMA model.

MAPE_VMDARIMA

Mean Absolute Percentage Error (MAPE) for VMDARIMA model.

rmse_VMDARIMA

Root Mean Square Error (RMSE) for VMDARIMA model.

References

Box, G. E., Jenkins, G. M., Reinsel, G. C. and Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley and Sons.

Dragomiretskiy, K.and Zosso, D. (2014). Variational mode decomposition. IEEE transactions on signal processing, 62(3), 531–544.

Wang, H., Huang, J., Zhou, H., Zhao, L. and Yuan, Y. (2019). An integrated variational mode decomposition and arima model to forecast air temperature. Sustainability, 11(15), 4018.

See Also

VMDTDNN,VMDELM

Examples

data("Data_Maize")
VMDARIMA(Data_Maize)