EFFICIENCY AND PRECISION OF STOCHASTIC MODELS IN AGRICULTURAL PRODUCTIVITY
DOI:
https://doi.org/10.55766/sujst5229Keywords:
Markov Process, ARMA Modeling, Oilseed, YieldAbstract
In this paper, we compare the Markov chain and ARMA models for predicting oilseed productivity, focusing on the area under cultivation, production, and yield. This study aims to evaluate the accuracy and efficiency of these two forecasting techniques and their capacity to predict future oilseed output patterns. Historical information on area, production, and yield was acquired and prepared for examination. The Markov chain model was created to depict the probability of changes between different productivity levels. On the other hand, the ARMA model was used to analyze time series data on the productivity of oilseeds, and suitable parameters for capturing temporal trends were discovered. Metrics including MAE, RMSE, MAPE, MSE, and SMAPE were used to evaluate the performance of both models. The research findings from the comparative analysis illuminate the pros and cons of each model, as well as their potential applications in forecasting oilseed output. Remarkably, the Markov chain model performed better in this experiment than the ARMA method.
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