Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Kernel-based machine learning methods are gaining increasing interest in flow modeling and prediction in recent years. Gaussian process (GP) is one example of such kernel-based methods, which can provide very good performance for nonlinear problems. In this work,  apply GP regression to flow modeling and prediction of athletes in ski races, but the proposed framework can be generally applied to other use cases with device trajectories of positioned data. Some specific aspects can be addressed when the data is periodic, like in sports where the event is split up over multiple laps along a specific track. Flow models of both the individual skier and a cluster of skiers are derived and analyzed. Performance has been evaluated using data from the Falun Nordic World Ski Championships 2015, in particular the Men's cross country 4 × 10 km relay. The results show that the flow models vary spatially for different skiers and clusters.  further demonstrate that GP regression provides porful and accurate models for flow prediction.
['Yuxin Zhao', 'Feng Yin', 'Fredrik Gunnarsson', 'Fredrik Hultkratz', 'Johan Fagerlind']
I am writing a research paper on a idea Gaussian processes for flow modeling and prediction of positioned trajectories evaluated with sports data
[{"from": "human", "value": "I am writing a research paper on a idea Gaussian processes for flow modeling and prediction of positioned trajectories evaluated with sports data"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Kernel-based machine learning methods are gaining increasing interest in flow modeling and prediction in recent years. Gaussian process (GP) is one example of such kernel-based methods, which can provide very good performance for nonlinear problems. In this work,  apply GP regression to flow modeling and prediction of athletes in ski races, but the proposed framework can be generally applied to other use cases with device trajectories of positioned data. Some specific aspects can be addressed when the data is periodic, like in sports where the event is split up over multiple laps along a specific track. Flow models of both the individual skier and a cluster of skiers are derived and analyzed. Performance has been evaluated using data from the Falun Nordic World Ski Championships 2015, in particular the Men's cross country 4 \u00d7 10 km relay. The results show that the flow models vary spatially for different skiers and clusters.  further demonstrate that GP regression provides porful and accurate models for flow prediction."}]