VMDARIMA | R Documentation |
The VMDARIMA function computes forecasted value with different forecasting evaluation criteria for Variational Mode Decomposition (VMD) Based Autoregressive Integrated Moving Average (ARIMA).
VMDARIMA (data, stepahead=10, nIMF=4, alpha=2000, tau=0, D=FALSE)
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. |
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.
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. |
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.
VMDTDNN,VMDELM
data("Data_Maize") VMDARIMA(Data_Maize)
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