Abstract
Wireless traffic prediction plays an important role in network planning and management, especially for real-time decision making and short-term prediction. Systems require high accuracy, low cost, and low computational complexity prediction methods. Although exponential smoothing is an effective method, there is a lack of use with cellular networks and research on data traffic. The accuracy and suitability of this method need to be evaluated using several types of traffic. Thus, this study introduces the application of exponential smoothing as a method of adaptive forecasting of cellular network traffic for cases of voice (in Erlang) and data (in megabytes or gigabytes). Simple and Error, Trend, Seasonal (ETS) methods are used for exponential smoothing. By investigating the effect of their smoothing factors in describing cellular network traffic, the accuracy of forecast using each method is evaluated. This research comprises a comprehensive analysis approach using multiple case study comparisons to determine the best fit model. Different exponential smoothing models are evaluated for various traffic types in different time scales. The experiments are implemented on real data from a commercial cellular network, which is divided into a training data part for modeling and test data part for forecasting comparison. This study found that ETS framework is not suitable for hourly voice traffic, but it provides nearly the same results with Holt–Winter’s multiplicative seasonal (HWMS) in both cases of daily voice and data traffic. HWMS is presumably encompassed by ETC framework and shows good results in all cases of traffic. Therefore, HWMS is recommended for cellular network traffic prediction due to its simplicity and high accuracy.
Highlights
This study found that ETS framework is not suitable for hourly voice traffic, but it provides nearly the same results with Holt– Winter’s multiplicative seasonal (HWMS) in both cases of daily voice and data traffic
Wireless traffic prediction is a key component of network planning, development, and management
A more comprehensive study was required to evaluate the usage of various exponential smoothing methods in more types of wireless traffic, such as data, which is becoming more important along with the development of communication technology
Summary
Wireless traffic prediction is a key component of network planning, development, and management. Analysis of wireless network traffic shows that the traffic series normally contains seasonal components and can be modeled and forecasted by time series analysis models (Tran, Ma, Li, Hao, & Trinh, 2015) Authors in these papers proposed combining statistical procedures for modeling and forecasting cellular network traffic, such as the autoregressive integrated moving average (ARIMA) and generalized autoregressive conditional heteroskedasticity (GARCH). Exponential smoothing methods have been applied in several areas, such as palm oil real production forecasting (Siregar, Butar-Butar, Rahmat, Andayani, & Fahmi, 2017), power (Usaratniwart, Sirisukprasert, Hatti, & Hagiwara, 2017), revenue forecasting (Rahman, Salma, Hossain, & Khan, 2016), and solar irradiance prediction (Margaret & Jose, 2015), to name a few These researchers all achieved good results with this low-complexity and low-cost method. Voice Traffic Estimation Outputs of AIC and AMSE Based ETS Chosen Models
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