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Traffic prediction based on time series analysis methods that are low-cost and low computational complexity can offer more efficient resource management and better QoS. Although exponential smoothing is such a kind of method, there is a lack of application in cellular networks and data traffic research, especially with the robust development of mobile Internet applications nowadays. Therefore, this study provides a comprehensive research on cellular network traffic prediction using exponential smoothing methods. More cases of traffic including voice and data in different time granularities as well as different domains compared with other studies are considered. Besides, more exponential smoothing methods are simultaneously investigated for different cases of traffic. Our multiple case study approach leads to a more convincing result of choosing the best fit model. Data collected from real commercial cellular networks is used for experiments to make the results more practical and persuasively. In our experiment method, the model has the lowest RMSE value is chosen among three types of method. The experiment results show that exponential smoothing methods outperform multiplicative seasonal ARIMA, which is slower and more complex in computation in all cases, so they should be recommended for traffic prediction.