Predictive User Engagement Modeling Using Time Series Analysis for Interactive Media and Gaming

With the rapid expansion of interactive media and gaming platforms, predicting user engagement has become crucial for enhancing player experience and optimizing content strategies. Traditional models struggle to capture the dynamic and evolving nature of user interactions. This study presents an advanced predictive model leveraging time series analysis and machine learning, integrating ARIMA, Kalman Filter, Random Forest, and LSTM to analyze engagement patterns in gaming and media datasets. Preprocessing includes timestamp conversion, anomaly detection, and resampling. Fourier and Wavelet Transforms are employed to capture periodic behaviors and engagement trends. Our ensemble model, integrating ARIMA, Kalman Filter, and LSTM, further improved accuracy (RMSE = 42.8, R² = 0.91), outperforming conventional approaches. The findings provide valuable insights for personalized content recommendations and user retention strategies. Future work will explore deep learning techniques to further refine engagement predictions and adaptive content delivery. This study demonstrates that integrating time series techniques with machine learning enhances engagement modeling in gaming platforms.