Advanced Time Series Modeling in Digital Payments: Harnessing Seasonal Patterns for Enhanced Forecasting
Keywords:
Time Series, Prophet, Exponential smoothing, SARIMA, XGBoost, LSTM, Model Evaluation.Abstract
In today's digital payments landscape, accurate forecasting of future trends has become essential. By obtaining reliable forecasts, organizations can gain numerous benefits: they can detect and prevent fraud, enhance operational efficiency, improve customer retention, optimize marketing campaigns, and deliver a more personalized customer experience. Given these advantages, companies are increasingly focusing on predictive analytics, making it crucial to select the right model for analyzing data effectively. In this study, we trained and tested five different models to evaluate their performance and efficiency on a digital payment’s dataset, which exhibits strong seasonal trends. Through this approach, we aim to determine the most suitable model to support strategic decision-making and drive business success in the competitive digital payments sector.
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