Comparison of Sarima and Lstm Model to Forecast the Comprehensive Income (Loss) of Life Insurances in Indonesia

This study compares the performance of the Seasonal Autoregressive Integrated Moving Average (SARIMA) and Long Short-Term Memory (LSTM) models in predicting time series data with seasonal patterns. Using historical data, namely total comprehensive income (loss) data for all insurance companies in Indonesia that contain strong seasonal components, the SARIMA model proved superior in capturing and representing seasonal patterns compared to LSTM. However, the evaluation results showed that the overall prediction accuracy of both models is still at a level that needs improvement. These findings suggest that although SARIMA is effective for seasonal data, the integration of statistical and deep learning-based models has the potential to improve prediction performance. Future research is recommended to explore hybrid approaches, hyperparameter optimization, and the use of external variables to improve the accuracy and generalization of predictive models.