Predicting consumer price index movements with SARIMA: a case from Kediri City

The city of Kediri plays a significant role in driving the regional economy. One factor that plays an important role in regional economic stability is the Consumer Price Index (CPI). This study aims to predict the Consumer Price Index (CPI) of Kediri City using the Seasonal ARIMA (SARIMA) model, taking into account seasonal patterns. The data used consists of monthly CPI secondary data from January 2020 to December 2023 obtained from the Central Statistics Agency (BPS). The analysis was conducted through several stages, including a stationarity test using the Augmented Dickey-Fuller (ADF) method, model identification using ACF and PACF, parameter estimation, model selection based on the Akaike Information Criterion (AIC) value, and forecasting. The results of the study indicate that the SARIMA(2,2,0)(1,0,0)[12] model has the lowest AIC value. With a narrow confidence interval and a stable trend, this model can accurately predict the CPI for 2024. The forecasting results show that the CPI continues to increase each year. It is hoped that these findings will contribute academically to seasonal time series modelling and assist the government and businesses in developing data-driven economic strategies in area.