Time Series Prediction for Food sustainability
It addresses food sustainability for global organizations and manufacturers, but is incremental as it applies an existing method to new data.
The paper tackles forecasting food shortages by predicting top products at risk in each country using a statistical regression model, achieving low absolute and root mean square errors.
With exponential growth in the human population, it is vital to conserve natural resources without compromising on producing enough food to feed everyone. Doing so can improve people's livelihoods, health, and ecosystems for the present and future generations. Sustainable development, a paradigm of the United Nations, is rooted in food, crop, livestock, forest, population, and even the emission of gases. By understanding the overall usage of natural resources in different countries in the past, it is possible to forecast the demand in each country. The proposed solution consists of implementing a machine learning system using a statistical regression model that can predict the top k products that would endure a shortage in each country in a specific period in the future. The prediction performance in terms of absolute error and root mean square error show promising results due to its low errors. This solution could help organizations and manufacturers understand the productivity and sustainability needed to satisfy the global demand.