利用自回归综合移动平均法 (ARIMA) 计算楠榜的消费价格指数 (CPI)

Mika Sitinjak, Nuramaliyah ‎
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引用次数: 0

摘要

消费者价格指数(CPI)是影响经济增长的一个指标。消费价格指数是一个计算家庭在一定时期内消费的一组商品和服务的平均价格变化的指数。消费物价指数也用于衡量一个国家的通货膨胀。通货膨胀通过消费物价指数的不时变化来描述。为了预测和尽量减少通货膨胀带来的经济风险,将对 CPI 数据进行预测。在本研究中,将使用 ARIMA(自回归整合移动平均)模型对未来 6 个月的 CPI 进行预测。研究结果表明,可用于预测 CPI 的 ARIMA 模型有 ARIMA (0,2,0)、ARIMA (0,2,1)、ARIMA (1,2,0) 和 ARIMA (1,2,1)。最佳模型的选择基于 AIC 值最小的模型。因此,用于预测 CPI 的最佳模型是 ARIMA 模型(0,2,1),其 AIC 值为 83.21。
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Indeks Harga Komsumen (IHK) di Lampung Menggunakan Autoregressive Integrated Moving Average (ARIMA)
The Consumer Price Index (CPI) is an indicator that influences economic growth. CPI is an index that calculates the average of price change of a group of goods and services consumed by households in a certain period of time. CPI is also used to measure inflation in a country. Inflation is described by changes in the CPI from time to time. To anticipate and minimize economic risks caused by inflation, forecasting will be carried out on CPI data. In this study, the CPI will be predicted for the next 6 months using the ARIMA (Autoregressive Integrated Moving Average) model. The result of this research shows that the ARIMA models that can be used to predict CPI are ARIMA (0,2,0), ARIMA (0,2,1), ARIMA (1,2,0), and ARIMA (1,2,1) . The selection of the best model is carried out based on the model that has the smallest AIC value. Based on this, the best model used to predict CPI is the ARIMA model (0,2,1) with an AIC value of 83.21. In addition, this model fulfills diagnostics with white noise residuals, so that forecasting results using this model will be more accurate.
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