Mobile data traffic forecasting in UMTS networks based on SARIMA model: The case of Addis Ababa, Ethiopia

Medhn, Samuel; Seifu, Bethelhem; Salem, Amel; Hailemariam, Dereje

doi:10.1109/afrcon.2017.8095496

Cited by 10 publications

(6 citation statements)

References 11 publications

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“…Variants of ARIMA [15] have also been proposed to handle different aspects of mobile traffic prediction, such as decomposing traffic into regularity and randomness components. Another variant, SARIMA, highlighted for its ability to capture seasonal patterns, was effectively used in [16] for improved mobile traffic prediction.…”

Section: A Mobile Traffic Predictionmentioning

confidence: 99%

Layer-Wise Personalized Federated Learning for Mobile Traffic Prediction

Lee,

Sung,

Shin

2024

IEEE Access

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With the evolution of mobile networks delivering high-performance network services to a myriad of devices, accurate mobile traffic prediction has become increasingly important. In recent years, federated learning (FL) has emerged as a communication-efficient approach, enabling collaborative model training without the centralized data aggregation. Despite its promising potential, FL-based mobile traffic prediction has following two major challenges: (1) Data heterogeneity across regions: The diverse communication and mobility patterns inherent to different regions can lead to uneven traffic distribution. Training on such heterogeneous data can result in the global model failing to capture the unique patterns of specific regions, compromising consistent prediction performance across all regions. (2) Communication efficiency concerns: The frequent exchange of large model weights during training leads to substantial signaling overhead in the FL. This added communication can pose a significant burden on the limited network bandwidth, potentially causing performance degradation in mobile networks. In this paper, we propose a novel personalized FL framework to address these challenges. Our framework enables a fine-grained federation through a layer-wise aggregation for the global model. This approach personalizes the global model to capture unique regional characteristics such as traffic spikes and other irregular patterns. In addition, we introduce an adaptive layer freezing mechanism to reduce communication costs during training. By selectively transmitting only the layers that require further training, our framework effectively enhances communication efficiency without sacrificing prediction performance. Extensive experiments on a realworld mobile traffic dataset demonstrate that our approach not only provides superior prediction accuracy compared to baselines but also achieves significant communication cost saving. INDEX TERMS Mobile traffic prediction, traffic management, personalized federated learning VOLUME x, 2023

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Section: A Mobile Traffic Predictionmentioning

confidence: 99%

Layer-Wise Personalized Federated Learning for Mobile Traffic Prediction

Lee,

Sung,

Shin

2024

IEEE Access

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“…ARIMA and SARIMA are two commonly used statistical approaches that is employed to forecast linear data. As presented in Reference 3, a model based on SARIMA forecasted 30‐day‐ahead of mobile data traffic in Addis Ababa, Ethiopia. A SARIMA model was introduced in Reference 22 to predict the daily and monthly solar radiation in Seoul, South Korea.…”

Section: Related Workmentioning

confidence: 99%

“…The statistical models concentrate on mathematical concepts and formulas to analyze the data. Studies like References 3 and 4 used the seasonal auto‐regressive integrated moving average (SARIMA) and auto‐regressive integrated moving average (ARIMA) models to forecast mobile data traffic, respectively. Although these statistical models forecast linear characteristics of time‐series data precisely, they are not able to capture nonlinear and complex patterns.…”

Section: Introductionmentioning

confidence: 99%

An optimized hybrid methodology for short‐term traffic forecasting in telecommunication networks

Alizadeh,

Beheshti,

Ramezani

et al. 2023

Trans Emerging Tel Tech

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With the rapid development of telecommunication networks, the predictability of network traffic is of significant interest in network analysis and optimization, bandwidth allocation, and load balancing adjustment. Consequently, in recent years, significant research attention has been paid to forecasting telecommunication network traffic. Telecommunication traffic forecasting problems can be considered a time‐series problem, wherein periodic historical data is fed as the input to a model. Time‐series forecasting approaches are broadly categorized as statistical machine learning (ML) methods and their combinations. Statistical approaches forecast linear characteristics of time‐series data, unable to capture nonlinear and complex patterns. ML‐based approaches can model nonlinear characteristics of data. In recent years, hybrid approaches combining statistical and ML‐based approaches have been widely used to model linear and nonlinear data characteristics. However, the performance of these approaches highly depends on feature selection techniques and hyper‐parameter tuning of ML methods. A novel hybrid method is proposed for short‐term traffic forecasting based on feature selection and hyperparameter optimization to address this problem. It combines statistical and ML methods to model linear and nonlinear components of data. First, a novel feature selection technique, modified mutual information based on a linear combination of targets, is proposed to find the candidate input variables. Next, a combination of vector auto regressive moving average (VARMA), long short‐term memory (LSTM), and multilayer perceptron (MLP), called VARMA‐LSTM‐MLP forecaster, is suggested to forecast short‐term traffic. A hybrid metaheuristic algorithm, composed of firefly and BAT, is employed to find the optimal set of hyper‐parameter values. The proposed method is assessed by a real‐world dataset containing Tehran city's daily telecommunication data in IRAN. The evaluation results demonstrate that the proposed method outperforms the existing methods in terms of mean squared error and mean absolute error.

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“…Diverse variations of ARIMA are proposed according to various applications and time-series; Among them, it is worth mentioning "Seasonal-ARIMA (SARIMA)" 25 and "Fractional AutoRegressive Integrated Moving Average (FARIMA)". 26 The former is often used in NTP considering its compatibility with the nature of changes in networks which usually obeys certain time patterns.…”

Section: Statistical Techniques For Ntpmentioning

confidence: 99%

From statistical‐ to machine learning‐based network traffic prediction

Lohrasbinasab

Shahraki

Taherkordi

et al. 2021

Trans Emerging Tel Tech

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Nowadays, due to the exponential and continuous expansion of new paradigms such as Internet of Things (IoT), Internet of Vehicles (IoV) and 6G, the world is witnessing a tremendous and sharp increase of network traffic. In such large‐scale, heterogeneous, and complex networks, the volume of transferred data, as big data, is considered a challenge causing different networking inefficiencies. To overcome these challenges, various techniques are introduced to monitor the performance of networks, called Network Traffic Monitoring and Analysis (NTMA). Network Traffic Prediction (NTP) is a significant subfield of NTMA which is mainly focused on predicting the future of network load and its behavior. NTP techniques can generally be realized in two ways, that is, statistical‐ and Machine Learning (ML)‐based. In this paper, we provide a study on existing NTP techniques through reviewing, investigating, and classifying the recent relevant works conducted in this field. Additionally, we discuss the challenges and future directions of NTP showing that how ML and statistical techniques can be used to solve challenges of NTP.

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Mobile data traffic forecasting in UMTS networks based on SARIMA model: The case of Addis Ababa, Ethiopia

Cited by 10 publications

References 11 publications

Layer-Wise Personalized Federated Learning for Mobile Traffic Prediction

Layer-Wise Personalized Federated Learning for Mobile Traffic Prediction

An optimized hybrid methodology for short‐term traffic forecasting in telecommunication networks

From statistical‐ to machine learning‐based network traffic prediction

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