Comparison of Arima and Holt-Winters forecasting models for time series of cereal production in Peru

Abstract

Agricultural commodities present remarkable volatility in their production levels, which severely affects farmers. The variational dynamics in the prices of the inputs used and the constant variations in weather conditions have a significant influence on the cereal production chain in Peru; therefore, compared to the ARIMA model, the Additive Holt-Winters forecasting model presented a better fit according to the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC), forecasting the production of Oryza sativa, Zea mays L. var. Indurata and Amaranthus caudatus; however, due to the high seasonality, volatility of production, and the greater amount of outliers due to production in certain periods and geographical areas, the Holt-Winters Multiplicative model predicted the national production of Zea mays L. ssp amiláceo and Chenopodium quinoa, in Peru in the period 2000-2021.