decompML - Decomposition Based Machine Learning Model
The hybrid model is a highly effective forecasting
approach that integrates decomposition techniques with machine
learning to enhance time series prediction accuracy. Each
decomposition technique breaks down a time series into multiple
intrinsic mode functions (IMFs), which are then individually
modeled and forecasted using machine learning algorithms. The
final forecast is obtained by aggregating the predictions of
all IMFs, producing an ensemble output for the time series. The
performance of the developed models is evaluated using
international monthly maize price data, assessed through
metrics such as root mean squared error (RMSE), mean absolute
percentage error (MAPE), and mean absolute error (MAE). For
method details see Choudhary, K. et al. (2023).
<https://ssca.org.in/media/14_SA44052022_R3_SA_21032023_Girish_Jha_FINAL_Finally.pdf>.
