Time Series Analysis for F1 Lap Time Perdiction: Focusing on Monaco and Italian Grand Prix

This study aims to predict lap time trends over the next five years in the Qualifying (Q1) sessions of the Monaco Grand Prix and Italian Grand Prix using the NeuralProphet model. Data from 2000 to 2024 were analyzed to investigate the impact of FIA technical regulation changes and advancements in vehicle performance on lap times. The results showed a continuous decrease in lap times for both circuits, with Monaco exhibiting a greater reduction due to its complex track characteristics and high-downforce setup requirements. In contrast, the Italian Monza circuit, characterized by long straights and low-downforce setups, showed a relatively smaller reduction. The potential impact of the new 1.6L V6 turbo engines and sustainable fuels, set to be introduced in 2026, was also considered. The study found similar lap time reduction patterns among teams using the same engine. Incorporating multivariate analysis, including external factors such as temperature, humidity, and tire conditions, could further enhance prediction accuracy. Future studies on diverse circuits with varying conditions, such as Mexico's high-altitude track or Singapore's humid environment, are suggested for generalization. This research expands the scope of F1 studies by leveraging the NeuralProphet model, contributing to improved race strategies, vehicle setups, and increased fan engagement, offering significant academic and practical implications.