A Comparative Study of Neural Networks and ANFIS for Forecasting Attendance Rate of Soccer Games

• Main Authors: Mehmet Şahin, Rızvan Erol
• Format: Article
• Language: English
• Published: MDPI AG 2017-11-01
• Series: Mathematical and Computational Applications
• Subjects: neural networks; sports attendance; occupancy rate; sports demand forecasting; sports demand rate; ANFIS
• Online Access: https://www.mdpi.com/2297-8747/22/4/43

The main purpose of this study was to develop and apply a neural network (NN) approach and an adaptive neuro-fuzzy inference system (ANFIS) model for forecasting the attendance rates at soccer games. The models were designed based on the characteristics of the problem. Past real data was used. Training data was used for training the models, and the testing data was used for evaluating the performance of the forecasting models. The obtained forecasting results were compared to the actual data and to each other. To evaluate the performance of the models, two statistical indicators, Mean Absolute Deviation (MAD) and mean absolute percent error (MAPE), were used. Based on the results, the proposed neural network approach and the ANFIS model were shown to be effective in forecasting attendance at soccer games. The neural network approach performed better than the ANFIS model. The main contribution of this study is to introduce two effective techniques for estimating attendance at sports games. This is the first attempt to use an ANFIS model for that purpose.