Safira Anisya, Dhiya'ulhaq Riswanda Ayu, Fahmiyah Indah, Ghani Mohammad
Data Science Technology, Faculty of Advanced Technology and Multidiscipline, Universitas Airlangga, Surabaya, 60115, Indonesia.
MethodsX. 2024 Jul 17;13:102867. doi: 10.1016/j.mex.2024.102867. eCollection 2024 Dec.
Inflation is one of macroeconomic issues in Indonesia that needs to be controlled. Inflation could happen because of widespread increases in the cost of goods and services. Annual inflation rate in Indonesia on 2008 to 2023 are quite fluctuating and several periods are not achieved inflation target yet. One of the ways to control inflation is by making predictions for the upcoming period. Java Island is the biggest contributor on economy and Gross Domestic Product (GDP) in Indonesia so it can be considered as general indicator to measure overall inflation rate of Indonesia. Thus, data used in this study is monthly inflation at each province in Java Island from January 2008 to December 2023. This study using two methods, Autoregressive Integrated Moving Average (ARIMA) for univariate time series prediction and Generalized Space-Time ARIMA (GSTARIMA) for multivariate time series prediction with a spatial factor. The results of both models will be compared to determine which model has better accuracy. Based on RMSE value, GSTARIMA model has least average RMSE value, which is 0.113 compared with ARIMA model which has average RMSE value 0.319 thus it can conclude that spatial factors addition could increase accuracy on inflation prediction in Java Island.•This paper purposes to get Java Island's inflation rate prediction to determine better policy on controlling cost of goods and services.•Best model using GSTARIMA methods is GSTARMA(1,1) with distance invese matrix that indicate that coordinate point of each location increase performance of inflation rate prediction.•The result indicate GSTARIMA has better accuracy than ARIMA for inflation prediction in Java Island based on RMSE value.
通货膨胀是印度尼西亚需要控制的宏观经济问题之一。通货膨胀可能是由于商品和服务成本普遍上涨而发生的。2008年至2023年印度尼西亚的年度通货膨胀率波动较大，有几个时期尚未实现通货膨胀目标。控制通货膨胀的方法之一是对未来时期进行预测。爪哇岛是印度尼西亚经济和国内生产总值（GDP）的最大贡献者，因此可以被视为衡量印度尼西亚总体通货膨胀率的一般指标。因此，本研究使用的数据是2008年1月至2023年12月爪哇岛各省份的月度通货膨胀数据。本研究使用两种方法，即用于单变量时间序列预测的自回归积分移动平均（ARIMA）和用于具有空间因素的多变量时间序列预测的广义时空ARIMA（GSTARIMA）。将比较两个模型的结果，以确定哪个模型具有更高的准确性。基于均方根误差（RMSE）值，GSTARIMA模型的平均RMSE值最小，为0.113，而ARIMA模型的平均RMSE值为0.319，因此可以得出结论，添加空间因素可以提高爪哇岛通货膨胀预测的准确性。•本文旨在获得爪哇岛的通货膨胀率预测，以确定控制商品和服务成本的更好政策。•使用GSTARIMA方法的最佳模型是GSTARMA(1,1)，其距离逆矩阵表明每个位置的坐标点提高了通货膨胀率预测的性能。•结果表明，基于RMSE值，GSTARIMA在爪哇岛通货膨胀预测方面比ARIMA具有更高的准确性。
