The seasonal model of chili price movement with the effect of long memory and exogenous variables for improving time series model accuracy - Devianto, Dodi; Wahyuni, Elsa; Maiyastri, Maiyastri; Yollanda, Mutia (Frontiers in Applied Mathematics and Statistics, 2024)
This study aimed to explore big time series data of agricultural commodities with an autocorrelation model comprising long-term processes, seasonality, and the impact of exogenous variables. Among the agricultural commodities with the large amount data, chili prices exemplified criteria for longterm memory, seasonality, and the impact of various factors on production as an exogenous variable.These factors included the month preceding the new year and the week before the Eid Al-Fitr celebration in Indonesia. To address the factors affecting price fluctuations, the SARFIMA model was used to manage seasonality and long-term memory effects in the big data analysis, it is improved by the addition of exogenous variables through SARFIMAX. After comparing the accuracy of both models, it was discovered that the SARFIMAX performed better, indicating the influence of seasonality and past chili prices for an extended period in conjunction with exogenous variables.The SARFIMAX model gives improvement on model accuracy by adding effect of exogenous variables. Consequently, this observation concerning price dynamics established the cornerstone for maintaining the sustainability of chili supply even with the big data case.