Data_Maize

Monthly international Maize price from Janurary 2001 to December 2021.

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>.

Kapil Choudhary

decompML

Decomposition Based Machine Learning Model

Girish Kumar Jha

Rajender Parsad

Ronit Jaiswal

Rajeev Ranjan Kumar

P Venkatesh

Dwijesh Chandra Mishra

Data_Maize function

A time series data with 252 observations.<dl>
 <dt><code>price</code></dt>
<dd>a time series</dd> 
</dl>

Format

Monthly International Maize Price — Data_Maize

A time series data with 252 observations.<dl>
 <dt><code>price</code></dt>
<dd>a time series</dd>

 
</dl>

Data_Maize: Monthly International Maize Price

Description

Usage

Arguments

Format

Details

References

Examples