A comprehensive research on exponential smoothing methods in modeling and forecasting cellular traffic

Abstract

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.

Real‐World Wireless Network Modeling and Optimization: From Model/Data‐Driven Perspective

“…ARIMA model and HW model are univariate time series models. In addition to these two models, there are ES method [ 13 ] and particle filter method based on sequence Monte Carlo (SMC) [ 14 ] for traffic demand prediction. As the classical method, the prediction accuracy of statistical model is much less than machine learning and deep learning model since most of them are based on the linear relationship between input values and output values.…”

“…For short-term flow forecast represented by seasonal problems, Wang et al [10] used MSE and MAE to evaluate the performance of the proposed ARMA model, and proved that short-term prediction has high prediction accuracy. The literature [12] proved that the prediction accuracy of conditional probability estimation model is higher, and the daily seasonal model error is 9.92%.…”

Improving Stock Price Prediction Using Combining Forecasts Methods

“…EWMA also known as single or simple exponential smoothing) is presented in [6], [7]. EWMA produces smoothing series…”

“…Some recent ARIMA based works encompass [1]- [4]. Smoothing methods are also efficient and much used till today, and some recent research study includes [5]- [7]. These comparatively basic and traditionally useful methods serve up to general-purpose expectation level of forecasting accuracy.…”

Special issue on applications and techniques in cyber intelligence 2019

“…Yang and Liu 11 draws on the theory of biological immune system, combines the ontology with the growth risk of start‐ups, and uses SWRL and Jess engine to build an ontology knowledge base of start‐ups' growth risk to identify various risks in the growth process of start‐up. Tran et al 12 provides a comprehensive research on cellular network traffic prediction using exponential smoothing methods. Guo and Chen 13 proposes an interval support vector domain description method through using the dynamic decreasing inertia weight particle swarm optimization.…”