Forecasting Paylater Usage in Central Java as A Digital Financial Innovation Using the ARIMA Model
Abstract
This study aims to forecast the monthly usage of PayLater services in Central Java as a representation of digital financial innovation within the growing digital economy. Using secondary data from October 2023 to October 2024, the analysis employs the ARIMA time series model to identify trends and generate short-term projections. The methodological process includes exploratory analysis, stationarity testing, differencing, model identification using ACF and PACF, parameter estimation, and diagnostic checking to ensure residual white noise. The selected ARIMA model is then used to produce forecasts for upcoming periods. The results indicate a consistent upward trend in PayLater adoption in Central Java, suggesting increasing public acceptance of Buy Now Pay Later (BNPL) services. These findings highlight the role of PayLater as an emerging form of digital financial innovation that supports economic activities and financial inclusion. The study also emphasizes the importance of forecasting for policymakers and fintech providers in anticipating demand, mitigating risk, and designing strategies aligned with regional digital transformation. Limitations related to the length of the dataset and the absence of seasonal variables are also discussed
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