Enhancing EV Market Forecasting Accuracy: Integrating Machine Learning with ARIMA Models

As the global push towards sustainability intensifies, electric vehicles (EVs) have become pivotal in shaping future transportation. Forecasting EV market trends is critical for optimizing resource allocation and infrastructure development. Existing forecasting models, however, struggle with rapidly changing market dynamics and often fail to integrate real-time data effectively, leading to significant forecasting errors. Addressing these challenges, our research introduces an enhanced auto-ARIMA model, significantly enhanced with machine learning (ML) techniques to dynamically adapt to new market data. This novel approach not only refines the prediction process by incorporating real-time market fluctuations but also improves the model’s responsiveness to external changes, such as policy shifts and economic variations. By integrating ML, our model continuously fine-tunes its parameters, ensuring it remains optimally aligned with the latest market conditions. The results from our study show a remarkable improvement in forecasting accuracy, with our enhanced auto-ARIMA model achieving a 15% better prediction rate compared to traditional methods and an additional 10% enhancement compared to auto-ARIMA models. This significant increase underscores the effectiveness of our approach in managing the complexities of the EV market.