SHORT-TERM TRANSACTIONS FORECASTING USING TIME SERIES ANALYSIS: A CASE STUDY FOR INDIA

This paper presents time series analysis for short-term Indian bank transactions (NEFT) forecasting. Two time series models are proposed, namely, the multiplicative decomposition model and the seasonal ARIMA Model. Forecasting errors of both models are computed and compared. The proposed models are implemented to predict one year transactions data. The accuracy of the two models are calculated and compared. The paper utilizes the mean absolute percentage error (MAPE) as a measure of forecast accuracy. Results show that both time series models can accurately predict the short-term Transactions load demand and that the Multiplicative decomposition model slightly outperforms the seasonal ARIMA model.