Developing a Hybrid ARIMA-XGBOOST Model for Analysing Mobile Money Transaction Data in Kenya

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This study formulated a hybrid ARIMA-XGBOOST model using the dataset from the Central Bank of Kenya and the objective was to formulate a Hybrid ARIMA-XGBOOST models to capture the patterns and dynamics in Mobile Money Transactions in Kenya. The study was motivated by the gaps that existed from the previous studies which lacked the ability to model both linear and non-linear trends in the data across time period. Since 2007 when Mpesa was invented, the rate of Mobile Money transactions has rose steadily and this required a complex model that will provide a clear trend. To accurately model the dynamics of Mobile Money Transactions in Kenya, this study presented a complex Hybrid model. The model was formulated by combining Autoregressive Integrated Moving Averages (ARIMA) and Extreme Gradient Boosting (XGBOOST). The ARIMA captures the linear trends of the data while the XGBOOST models the non-linear part and trained to model the ARIMA residuals. The model parameters were evaluated using Mean Absolute Error, Root Mean Square Error among others and confirmed to be accurate and reliable. Based on the findings from the ADF coefficient, the stationary condition was met (P-value=0.01), and therefore we proceeded to develop the ARIMA models. Initial diagnoses included model identification and examination of autocorrelation to determine the ARIMA configurations, whereas the Box-Jenkins test confirmed the models' adequacy (P-value=2.220e-16). Based on Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Percentage Error (MPE), which are all below 1 percent, indicates that the performance of the models had high prediction accuracy.