Bayesian Time Series Modeling for Market Shares Using R-INLA

The project aims to enhance the understanding and prediction of market shares for a statistical consultancy. By applying Bayesian compositional time series models, the research focuses on accurately forecasting market dynamics and providing insights into competitive positioning. The project leverages historical market data, economic indicators, and industry trends to build comprehensive models using the INLA framework. Applications include competitive analysis, market trend identification, and strategic planning. The findings are expected to inform decision-making processes and improve the consultancy's market strategies..