The fitness and leisure industry is a part of the sports industry. The sports industry is a huge industry group, such as sports equipment manufacturing, sports events, etc. The fitness and leisure industry is an industry that caters to the public with the continuous increase of people's living income. The purpose of this paper is to study the construction and demonstration of the output value forecasting model of the regional fitness and leisure industry. And use the GM model (1, N) to make a gray forecast model for each year's GDP, and judge the rationality of the model by comparing it with the data of previous years. Forecast GDP from 2017 to 2022. By comparing and analyzing the results obtained by the two forecasting methods, it is concluded that the forecasting results of the two forecasting methods have high accuracy, and the fluctuation of the forecast error of the structure value is relatively small. However, the gray system is simpler than regression analysis. The average relative error of the output value prediction of the fitness and leisure industry in the gray system area is 2.104%. It can be seen that the gray system prediction method has great application value in structural prediction.

Yuan Shao. The Output Value Prediction Model of Regional Fitness and Leisure Industry. International Journal of Frontiers in Sociology (2022), Vol. 4, Issue 3: 36-41. https://doi.org/10.25236/IJFS.2022.040306.

[1] Kuykendall T. EIA forecasts US coal production will hit 4-decade low in 2020. Snl Energy Coal Report, 2019, 15(3):7-8.

[2] Ozoegwu C G. Artificial neural network forecast of monthly mean daily global solar radiation of selected locations based on time series and month number. Journal of Cleaner Production, 2019, 216(APR.10):1-13.

[3] Cassagnole M, Ramos M H, Zalachori I, et al. Impact of the quality of hydrological forecasts on the management and revenue of hydroelectric reservoirs – a conceptual approach. Hydrology and Earth System Sciences, 2021, 25(2):1033-1052.

[4] Kristanto D, Jagadita I S. An Integrated Analysis for Post Hydraulic Fracturing Production Forecast in Conventional Oil Sand Reservoir. Journal of Earth Energy Engineering, 2021, 10(1):1-17.

[5] Carriere T, Kariniotakis G. An Integrated Approach for Value-Oriented Energy Forecasting and Data-Driven Decision-Making Application to Renewable Energy Trading. IEEE Transactions on Smart Grid, 2019, 10(6):6933-6944.

[6] Duc-Anh, An-Vo, Kate, et al. Value of seasonal climate forecasts in reducing economic losses for grazing enterprises: Charters Towers case study. The Rangeland Journal, 2019, 41(3):165-175.

[7] Pearre N S, Swan L G. Statistical approach for improved wind speed forecasting for wind power production. Sustainable Energy Technologies and Assessments, 2018, 27(JUN.):180-191.

[8] Pandey A K, Chingtham P, Prajapati S K, et al. Recent seismicity rate forecast for North East India: An approach based on rate state friction law. Journal of Asian earth sciences, 2019, 174(MAY 1):167-176.

[9] Ahmad S K, Hossain F. Maximizing energy production from hydropower dams using short-term weather forecasts. Renewable energy, 2020, 146(2):1560-1577.

[10] Gomes A, E Araújo, Souza O M, et al. Urban mobility: service level and demand forecast study on a road in the city of So Paulo. Independent Journal of Management & Production, 2019, 10(4):1341.

[11] Guerrero-Villar F, Dorado-Vicente R, Fike M, et al. Influence of ambient conditions on wind speed measurement: Impact on the annual energy production assessment. Energy Conversion & Management, 2019, 195(SEP.):1111-1123.

[12] Kim Y J, Lee D J. Regional Characteristics and Status of the Growth of the Korea's Tourism Industry. Journal of Tourism and Leisure Research, 2020, 32(2):55-76.