Time Series Prediction for Food sustainability

With over 7.9 billion humans, Extensive research has been performed in the field of machine it is getting harder for the majority of the population learning for social science to discover new findings, to lead a healthy life. Around 9.9% of the population, which understand the causal effects, and make predictions. Scholars accounts for 811 million people, still go to bed on an empty have experimented with various traditional mathematical stomach. On the contrary, over 1.3 billion tonnes of food are models, machine learning models and deep learning wasted every year. The world's population is rapidly growing, methods for food demand forecasting. Some of the popular and it is estimated that there will be around 10 billion choices include ARIMA, Holt-Winters, supervised regression people on Earth by the year 2050. Environmentalists have models, and artificial neural networks like NARXNN been trying to find solutions to reduce the numbers in terms (non-linear auto regressive exogenous neural network). of hunger and food wastage. Sustainable food development The research (Lutoslawski et al. 2021) uses a nonlinear ensures that the current and future human population has autoregressive neural network for food demand prediction.