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Research on wind speed prediction for Formula 1 grand prix based on multi-scale decomposition and time series neural network model
- 1 Beijing Institute of graphic communication
- 2 Beijing Institute of graphic communication
* Author to whom correspondence should be addressed.
Abstract
Wind speed variations have a significant impact on the aerodynamic characteristics of F1 racing cars and the race outcomes. To enhance the accuracy and stability of wind speed forecasting, this paper proposes a method based on multi-scale decomposition and time series neural network models. Initially, wind speed data collected from F1 Grand Prix events between 2018 and 2023 are subjected to Empirical Mode Decomposition (EMD), which decomposes the complex nonlinear and non-stationary time series data into several Intrinsic Mode Functions (IMFs) with different time scales. Subsequently, a Long Short-Term Memory network (LSTM) is utilized to predict each IMF component, and the predicted results of all IMFs are reconstructed into the overall wind speed forecast. Experimental results demonstrate that the method based on multi-scale decomposition and time series neural network models significantly outperforms traditional models such as Random Forest, Gradient Boosting Decision Tree (GBDT), and single LSTM models in terms of forecasting accuracy and stability. This study offers a novel perspective and approach for wind speed prediction in F1 Grand Prix events, with important theoretical significance and practical application value.
Keywords
Multi-scale Decomposition, Time Series Neural Network, Wind Speed Prediction
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Cite this article
Chen,H.;Zhao,E. (2025). Research on wind speed prediction for Formula 1 grand prix based on multi-scale decomposition and time series neural network model. Theoretical and Natural Science,95,8-17.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 2nd International Conference on Applied Physics and Mathematical Modeling
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