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

Şahin, Mehmet; Erol, Rızvan

doi:10.3390/mca22040043

Cited by 87 publications

(49 citation statements)

References 49 publications

(37 reference statements)

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“…Villa et al, 2011;Winfree et al, 2004). I found only two studies that predict stadium attendance without relying on information that is not accessible before a game has started and both use artificial neural network models to forecast short-run soccer match attendance rates (Şahin & Erol, 2017;Strnad et al, 2017).…”

Section: Predicting Game Attendance and Determinants Of Demandmentioning

confidence: 99%

Pre- and within-season attendance forecasting in Major League Baseball: a random forest approach

Mueller

2020

Applied Economics

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This study explores the forecasting of Major League Baseball game ticket sales and identifies important attendance predictors by means of random forests that are grown from classification and regression trees (CART) and conditional inference trees. Unlike previous studies that predict sport demand, I consider different forecasting horizons and only use information that is publicly accessible in advance of a game or season. Models are trained using data from 2013 to 2014 to make predictions for the 2015 regular season. The static within-season approach is complemented by a dynamic month-ahead forecasting strategy. Out-of-sample performance is evaluated for individual teams and tested against least-squares regression and a naive lagged attendance forecast. My empirical results show high variation in team-specific prediction accuracy with respect to both models and forecasting horizons. Linear and tree-ensemble models, on average, do not vary substantially in predictive accuracy; however, OLS regression fails to account for various team-specific peculiarities.

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Section: Predicting Game Attendance and Determinants Of Demandmentioning

confidence: 99%

Pre- and within-season attendance forecasting in Major League Baseball: a random forest approach

Mueller

2020

Applied Economics

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“…The researchers found that the performance of ANN is better than (LRM). Sahin and Erol (2017) conclude that ANFIS has an advantage over both Artificial Neural Network (ANN) and Fuzzy Inference Systems (FIS). In their study, a combination of both ANN and FIS (ANFIS) result in a better-developed method.…”

Section: Results Of Anfis For Estimated Peak Load Demandmentioning

confidence: 99%

GCC electrical long-term peak load forecasting modeling using ANFIS and MLR methods

Al-Hamad¹,

Qamber

2019

Arab Journal of Basic and Applied Sciences

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It is important to develop a suitable model to calculate electricity demand forecasting requested by decision makers. The present study deals with the electrical long-term peak load demand forecasting using a developed Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multiple Linear Regression (MLR) methods. The MLR model is formulated as a function of population and Gross Domestic Product (GDP) for the Gulf Cooperation Council (GCC) region. The Neuro-Fuzzy is thereafter trained using previous sets of data. This training gives a future annual electricity load prediction. The results obtained from the developed models have an acceptable level of mean errors. In general, the GCC region has high-energy consumption influenced by a number of factors, such as population and GDP. The annual variation of both population and GDP growth scenarios is based on development in the country. The obtained results will encourage the GCC through the energy field development and setting the future plans for it. The novelty of the present study is to avoid an increase in generation capacity in mid-term and long-term plans, which will help the GCC countries to avoid load shedding and meet the energy demands in different sectors. The developed models will help the economic development of the GCC countries. It also helps with finding the optimum time for electrical energy trading. The obtained results for the GCC illustrate the average percentage error calculated which was found to be close to 2% and 0.53% in multilinear regression and Neuro-Fuzzy, respectively. These results reduce capital investment, limiting the equipment installed and the expected load needed for better load distribution in the region. In conclusion, the Neuro-Fuzzy is the most accurate technique compared with MLR to estimate future electricity demand and, at the same time, it can be used in power system planning and development.

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“…It has been used in numerous applications and presentations of various fields (like mechanics, physics, economics, biology, industry and others) (e.g., [95][96][97][98][99][100][101][102][103][104][105], to name just a few recent). It has also been studied by other authors or compared with other neural networks (e.g., [103,106,107]). Its main advantage lies on the fact that it automatically adjusts the membership functions to our available data in order to achieve the best performance of our system.…”

Section: Why Anfismentioning

confidence: 99%

Global Image Thresholding Adaptive Neuro-Fuzzy Inference System Trained with Fuzzy Inclusion and Entropy Measures

Bogiatzis

Papadopoulos

2019

Symmetry

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Thresholding algorithms segment an image into two parts (foreground and background) by producing a binary version of our initial input. It is a complex procedure (due to the distinctive characteristics of each image) which often constitutes the initial step of other image processing or computer vision applications. Global techniques calculate a single threshold for the whole image while local techniques calculate a different threshold for each pixel based on specific attributes of its local area. In some of our previous work, we introduced some specific fuzzy inclusion and entropy measures which we efficiently managed to use on both global and local thresholding. The general method which we presented was an open and adaptable procedure, it was free of sensitivity or bias parameters and it involved image classification, mathematical functions, a fuzzy symmetrical triangular number and some criteria of choosing between two possible thresholds. Here, we continue this research and try to avoid all these by automatically connecting our measures with the wanted threshold using some Artificial Neural Network (ANN). Using an ANN in image segmentation is not uncommon especially in the domain of medical images. However, our proposition involves the use of an Adaptive Neuro-Fuzzy Inference System (ANFIS) which means that all we need is a proper database. It is a simple and immediate method which could provide researchers with an alternative approach to the thresholding problem considering that they probably have at their disposal some appropriate and specialized data.

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

Cited by 87 publications

References 49 publications

Pre- and within-season attendance forecasting in Major League Baseball: a random forest approach

Pre- and within-season attendance forecasting in Major League Baseball: a random forest approach

GCC electrical long-term peak load forecasting modeling using ANFIS and MLR methods

Global Image Thresholding Adaptive Neuro-Fuzzy Inference System Trained with Fuzzy Inclusion and Entropy Measures

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