Boosting time series forecasting accuracy with Holt-Winters and artificial neural networks

Abstract The Holt-Winters multiplicative method, when combined with artificial neural networks, offers a significant advancement in agricultural forecasting techniques. This hybrid model provides critical insights into key agricultural indicators, such as crop health, soil moisture levels, and backscatter values. By accurately predicting backscatter values, the model enables the early identification of potential challenges, including pest infestations and drought conditions. This allows farmers to implement proactive strategies, enhancing yields and operational efficiency. This innovative approach leverages the seasonal pattern-capturing capabilities of the Holt-Winters multiplicative method, incorporating a new initial value condition for improved accuracy. Combined with the flexibility and adaptability of artificial neural networks, the hybrid model effectively captures the complex and dynamic nature of agricultural data, delivering precise and reliable predictions. The model's effectiveness was assessed using standard performance metrics, including Mean Squared Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). Experimental results demonstrate that the hybrid model outperforms traditional methods, providing superior accuracy in forecasting backscatter values. This robust forecasting tool empowers farmers and agricultural professionals to make data-driven decisions, optimize resource utilization, and enhance productivity.