An Intelligent Prediction for Sports Industry Scale Based on Time Series Algorithm and Deep Learning

Area of Science:

• Sports Analytics
• Econometrics
• Data Science

Background:

• Existing models face challenges in accurately analyzing and predicting the sports industry's scale.
• The sports industry's economic dynamics require sophisticated analytical tools for effective policy-making.

Purpose of the Study:

• To optimize existing models for sports industry scale analysis and prediction.
• To leverage deep learning and time series theory for enhanced forecasting accuracy.
• To provide theoretical support for sports industry policy formulation.

Main Methods:

• Optimization of existing models using deep learning and time series theory.
• Application of the MNTS (Multivariate-Multivariate Time Series) structural algorithm for single-factor analysis.
• Development of a multistep time series prediction model.

Main Results:

• The MNTS algorithm demonstrates a high correlation fitting degree for single-factor sports industry scale analysis.
• The proposed optimization model effectively captures both global and local data trends.
• Multistep time series prediction shows significantly higher accuracy than single-step prediction.
• Time's impact on the sports industry scale is evident through fluctuating prediction curves.

Conclusions:

• The developed multistep time series prediction model accurately characterizes and forecasts the sports industry scale.
• The optimized approach provides a robust framework for sports industry economic analysis.
• This research offers valuable theoretical insights for sports policy development and implementation.