Abstract
Hosting major sporting events, such as the FIFA World Cup, is intended to generate positive outcomes for the host country and related industries. The sports and event organizing business is highly competitive, with technology and innovation playing crucial roles. To succeed and reap the benefits of organizing such an event, businesses must possess the ability to compete technologically and respond to market needs in a timely manner. In this study, we used Robust Forecasting Models to achieve our objectives. Our analysis involved conducting a time-series analysis using the number of patent applications related to recording and monitoring technologies. The results yielded effective forecasting methods for each prediction time frame. According to the findings of the study, the seasonal exponential smoothing method demonstrated superior predictive ability when dealing with data containing a seasonal pattern, while the moving average approach showed improved prediction results when there were fluctuating data due to limited availability for training. Consequently, the results of this study can aid R&D managers in making informed decisions and predictions regarding market demand for technological advancements in sports and related sectors.
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Manoli, N., Takahashi, M., Matsuura, Y. (2024). A Study on Time Series Approach Applications for Optimized Forecasting of Mega Sporting Events. In: Uden, L., Ting, IH. (eds) Knowledge Management in Organisations. KMO 2024. Communications in Computer and Information Science, vol 2152. Springer, Cham. https://doi.org/10.1007/978-3-031-63269-3_9
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DOI: https://doi.org/10.1007/978-3-031-63269-3_9
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