Novel time series methods in economic forecasting: SutteARIMA evidence from Indonesian Consumer Price Index and currency exchange rates

Abstract

Economic forecasting demands precision. Yet traditional models often stumble when confronted with real-world complexity – the messy interplay of linear trends, non-linear disruptions and seasonal fluctuations that characterize financial time series. This study introduces SutteARIMA, a hybrid forecasting approach that marries the a-Sutte Indicator with ARIMA methodology. What makes this interesting? The model does not require users to distinguish between linear and non-linear data patterns beforehand – a practical advantage in volatile economic environments. Testing across 200 pseudo samples revealed SutteARIMA’s versatility across diverse pattern types. The real test came with Indonesian economic indicators during the COVID-19 period. Results speak clearly. For Indonesia’s exchange rate forecasting, SutteARIMA achieved MSE of 66,474.88 and MAPE of 1.33% – outperforming ARIMA by 16%. Consumer Price Index (CPI) forecasting proved even more impressive: MSE of 0.0493 and MAPE of 0.1594%, surpassing a-Sutte by 17%. These performance gains are not marginal; they represent substantial improvements in forecasting accuracy during periods of economic uncertainty. The implications extend beyond technical superiority. Policymakers working with limited preprocessing resources can deploy SutteARIMA confidently across various economic indicators. The method’s consistent performance across both trending exchange rates and stable price indices suggests robust applicability in diverse economic contexts.