Long monthly temperature series and the Vector Seasonal Shifting Mean and Covariance Autoregressive model

This study aimed to explore big-time series data on agricultural commodities with an autocorrelation model comprising long-term processes, seasonality, and the impact of exogenous variables. Among the agricultural commodities with a large amount of data, chili prices exemplified criteria for long-term 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 Seasonal Autoregressive Fractionally Integrated Moving Average (SARFIMA) model was used to manage seasonality and long-term memory effects in the big data analysis. It improved with the addition of exogenous variables called SARFIMAX (SARFIMA with exogenous variables is known as SARFIMAX). After comparing the accuracy of both models, it was discovered that the SARFIMAX performed better, indicating the influence of seasonality and previous chili prices for an extended period in conjunction with exogenous variables. The SARFIMAX model gives an improvement in model accuracy by adding the 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.

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Long monthly temperature series and the Vector Seasonal Shifting Mean and Covariance Autoregressive model

Cited by 3 publications

References 31 publications

Long monthly European temperature series and the North Atlantic Oscillation

Long monthly European temperature series and the North Atlantic Oscillation

Introduction to the Themed Issue on Climate Econometrics

The seasonal model of chili price movement with the effect of long memory and exogenous variables for improving time series model accuracy

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