Forecasting of Coconut Price Using Time Series Modelling Technique

Coconut cultivation is widespread in India, particularly in the coastal regions, with Kerala playing a pivotal role in the country's coconut production.Accurate forecasting of coconut prices is crucial for strategic planning and decision-making among farmers, agribusinesses, and policymakers.In this study, time series forecasting is employed to predict future coconut price of Kerala by analysing historical data trends.Auto Regressive Integrated Moving Average (ARIMA) models are utilized in this research for effectively capturing discernible patterns found in historical data.The Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) play a crucial role in understanding the correlation structure within the time series.They assist in identifying the order of Autoregressive (AR) and Moving Average (MA) processes incorporated into the forecasting model.Multiple combinations of AR(p) and MA (q) orders were tested, and the best model is selected based on the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), resulting in the ARIMA (1, 1, 0) with drift model.Forecasted prices are compared with actual prices using metrics such as Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) to evaluate the model's accuracy.This assessment process provides insights into the effectiveness of the ARIMA (1, 1, 0) with drift model in capturing and predicting fluctuations in coconut prices.The study aims to empower stakeholders in the coconut industry with valuable information, enabling them to make informed decisions amidst dynamic agricultural conditions and market dynamics.