Cracking the Code of Crop Growth: Illuminating the Future of Philippines’ Onion Production for a Resilient Filipino Diet with the ARMA Forecasting Model (Published)
This study employed the Box-Jenkins methodology and the Autoregressive Moving Average (ARMA) model to forecast onion production in the Philippines. By utilizing historical data from the Philippine Statistics Authority, an optimal forecasting solution was achieved through the selection of the ARMA (4,2) model. The model demonstrated a favorable fit, passing diagnostic tests and exhibiting a mean absolute percentage error (MAPE) of 10.406%. Projections for onion production in 2023 and 2024 were provided, highlighting expected yields for each quarter. The analysis of historical data revealed periodic fluctuations in onion supply driven by factors such as weather patterns, market demand, agricultural practices, and imports or exports. The study’s implications emphasize the value of accurate forecasting models for decision-making in production planning, resource allocation, pricing, and market positioning. Policymakers, farmers, and stakeholders can utilize the findings to optimize onion production sustainably and enhance the agricultural sector’s performance in the Philippines.
Keywords: Agriculture, Box-Jenkins analysis, Forecast, Python, autoregressive moving average (ARMA) model, log transformation, onion production, onion supply, variance, variance stabilization
Forecasting Gross Domestic Product in the Philippines Using Autoregressive Integrated Moving Average (ARIMA) Model (Published)
Gross domestic product (GDP) plays a vital role in providing valuable insights into the size and performance of an economy. The GDP in the Philippines has shown steady growth over the years, reflecting the country’s economic development and progress. This paper presents a GDP forecast for the next eight years in the Philippines using Autoregressive Integrated Moving Average (ARIMA) model. This study aims to develop an optimal ARIMA model using the Box-Jenkins Methodology, incorporating a range of tests and selection criteria. The ARIMA (1,2,1) model is a valid choice for forecasting GDP in the Philippines, supported by its accuracy, as evidenced by the acceptable MAPE and high R-squared value. The model successfully captures patterns and trends in the GDP data, despite the significant variability represented by the sigma-squared value. The forecasted GDP for 2022-2029 suggests a positive outlook with a steady growth trajectory. These findings have important implications for economic planning, policy-making, and decision-making in the Philippines, as the forecasted GDP provides insights into the country’s future growth and development, influencing investment decisions, government strategies, and overall economic stability.
Keywords: Box Jenkins methodology, Forecast, Gross Domestic Product (GDP), autoregressive integrated moving average (ARIMA), mean absolute percentage error, time series analysis.