Web Application Integrating Time Series Models for Forecasting Corn Production

This study investigates the efficacy of various time series models in forecasting corn production in utilizing localized datasets from a Philippine province. We apply and evaluate models including Seasonal Autoregressive Integrated Moving Average (SARIMA), Bayesian SARIMA, Holt-Winters Exponential Smoothing, and Long Short-Term Memory for its predictive accuracy. Among these models, SARIMA emerges as the top performer, closely followed by Holt-Winters. LSTM and Bayesian SARIMA exhibit varied performances. The study emphasizes the importance of tailored forecasting methods for agricultural decision-making, highlighting SARIMA’s effectiveness in capturing seasonal patterns in corn production. The models are then integrated into a web application for ease of use and presentation of results.