Bayesian Mean-Drifted Model for Fertilizer Demand: A Comparative Study with Machine Learning Methods

Music City Center 

Forecasting fertilizer demand is critical for sustainable agriculture and efficient resource management. However, it remains a challenge due to non-stationary time series data with significant fluctuations. This study addresses this challenge by analyzing annual fertilizer demand in Canada spanning 1961 to 2022. We employ a comparative framework, integrating traditional and advanced statistical methods, including Random Forest Regression (RFR), Long Short-Term Memory (LSTM) networks, and a Bayesian Mean-Drifted Model (BMDM). While RFR captures non-linear relationships and LSTM handles temporal dependencies, the proposed BMDM accounts for time-varying mean shifts and uncertainties inherent in the data, offering a robust probabilistic framework. Comparative results between the BMDM, RFR, and LSTM models are presented. The findings highlight the importance of incorporating Bayesian methods for non-stationary time series forecasting, providing actionable insights for policymakers and agricultural stakeholders. This study advances fertilizer demand forecasting literature, highlighting adaptive models for global agricultural and environmental challenges.

Bayesian Mean Drifted Model

Fertilizer Demand Forecasting

LSTM Network

Non-Stationary Time Series

Random Forest Regression 

Section on Bayesian Statistical Science