ChronoCast: A Modular Forecasting Extension for Content Engagement

Time series forecasting plays a crucial role in various domains such as finance, weather prediction, and demand analysis. However, traditional machine learning models often lack transparency, making it difficult for users to understand the reasoning behind predictions. This paper proposes ChronoCast, an explainable and modular time series forecasting framework that combines multiple machine learning models using an ensemble approach to improve prediction accuracy. The system incorporates advanced feature engineering techniques to capture temporal patterns such as trends and seasonality. Additionally, explainability is integrated using model interpretation methods to provide insights into the factors influencing predictions. The proposed framework enhances both performance and user trust by delivering accurate and interpretable results. Experimental evaluation demonstrates that ChronoCast outperforms individual models in terms of prediction accuracy while maintaining transparency, making it suitable for real-world decision-making applications.