Enhanced Market Forecasting for Autonomous Vehicles Using a Hidden Mixture Gaussian Markov Model

In the context of the rapid development of autonomous vehicle technology and its increasing influence on the global automotive industry, the ability to accurately forecast market trends has become crucial for strategic decision-making. This study introduces the Hidden Mixture Gaussian Markov Model (HMGMM), a novel probabilistic framework designed to enhance the precision of market trend predictions in the autonomous vehicle sector. By addressing the limitations of traditional Hidden Markov Models (HMMs), which struggle with high-dimensional continuous data and dynamic market fluctuations, the HMGMM integrates Gaussian distributions to better capture the complexities of market dynamics. Utilizing a sliding time window mechanism and an improved algorithm for parameter dynamic updates, the HMGMM significantly improves response speed to market changes. The research employs experimental analysis on real-world datasets to validate the model’s effectiveness, demonstrating superior predictive performance with an accuracy of 0.892, recall of 0.901, and reduced RMSE of 0.144. These results highlight the potential of HMGMM as a reliable tool for market trend prediction, emphasizing the need for both the automotive industry and market analysts to adopt advanced probabilistic models to anticipate future market shifts and capitalize on the opportunities presented by autonomous vehicle technology.