Big Data Driven Mobile Traffic Understanding and Forecasting: A Time Series Approach

Xu, Fengli; Lin, Yujun; Huang, Jiaxin; Wu, Di; Shi, Hongzhi; Song, Jeungeun; Li, Yong

doi:10.1109/tsc.2016.2599878

Cited by 197 publications

(104 citation statements)

References 23 publications

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“…This approach reduces significantly the number of parameters in the model and enhances its ability to handle spatio-temporal data, which is particularly important to our problem. Further, ConvLSTMs can capture long-term trends present in sequences of data points, which makes them particularly well-suited to making inferences about mobile data traffic, as it known this exhibits important spatio-temporal correlations [29,33].…”

Section: The Spatio-temporal Networkmentioning

confidence: 99%

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Long-Term Mobile Traffic Forecasting Using Deep Spatio-Temporal Neural Networks

Zhang¹,

Patras²

2018

Proceedings of the Eighteenth ACM International Symposium on Mobile Ad Hoc Networking and Computing

214

121

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Forecasting with high accuracy the volume of data traffic that mobile users will consume is becoming increasingly important for precision traffic engineering, demand-aware network resource allocation, as well as public transportation. Measurements collection in dense urban deployments is however complex and expensive, and the post-processing required to make predictions is highly non-trivial, given the intricate spatio-temporal variability of mobile traffic due to user mobility. To overcome these challenges, in this paper we harness the exceptional feature extraction abilities of deep learning and propose a Spatio-Temporal neural Network (STN) architecture purposely designed for precise network-wide mobile traffic forecasting. We present a mechanism that fine tunes the STN and enables its operation with only limited ground truth observations. We then introduce a Double STN technique (D-STN), which uniquely combines the STN predictions with historical statistics, thereby making faithful long-term mobile traffic projections. Experiments we conduct with real-world mobile traffic data sets, collected over 60 days in both urban and rural areas, demonstrate that the proposed (D-)STN schemes perform up to 10-hour long predictions with remarkable accuracy, irrespective of the time of day when they are triggered. Specifically, our solutions achieve up to 61% smaller prediction errors as compared to widely used forecasting approaches, while operating with up to 600 times shorter measurement intervals.

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Section: The Spatio-temporal Networkmentioning

confidence: 99%

“…Mobile Traffic Forecasting: Several time series prediction schemes have been proposed to understand and predict mobile traffic dynamics [20,27,33]. Widely used prediction techniques such as Exponential Smoothing [27] and ARIMA [33] employ linear time series regression.…”

Section: Related Workmentioning

confidence: 99%

Long-Term Mobile Traffic Forecasting Using Deep Spatio-Temporal Neural Networks

Zhang¹,

Patras²

2018

Proceedings of the Eighteenth ACM International Symposium on Mobile Ad Hoc Networking and Computing

214

121

View full text Add to dashboard Cite

show abstract

“…Lots of researchers have investigated network traffic prediction that is instructive for congestion control, predictive network planning, and intelligent routing [10][11][12][13][14]. The existing network traffic prediction techniques consist of four categories: linear time series methods, nonlinear time series methods, hybrid model methods, and decomposition model methods.…”

Section: Related Workmentioning

confidence: 99%

“…After that, all these components are predicted by a trained BPNN using the QGA. The authors in [14] decompose the traffic of a large scale cellular network into regular and random components by a classic time series decomposition method.…”

Section: Related Workmentioning

confidence: 99%

Network Traffic Prediction Based on Deep Belief Network and Spatiotemporal Compressive Sensing in Wireless Mesh Backbone Networks

Nie

Wang

Wan

et al. 2018

Wireless Communications and Mobile Computing

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Wireless mesh network is prevalent for providing a decentralized access for users and other intelligent devices. Meanwhile, it can be employed as the infrastructure of the last few miles connectivity for various network applications, for example, Internet of Things (IoT) and mobile networks. For a wireless mesh backbone network, it has obtained extensive attention because of its large capacity and low cost. Network traffic prediction is important for network planning and routing configurations that are implemented to improve the quality of service for users. This paper proposes a network traffic prediction method based on a deep learning architecture and the Spatiotemporal Compressive Sensing method. The proposed method first adopts discrete wavelet transform to extract the low-pass component of network traffic that describes the long-range dependence of itself. Then, a prediction model is built by learning a deep architecture based on the deep belief network from the extracted low-pass component. Otherwise, for the remaining high-pass component that expresses the gusty and irregular fluctuations of network traffic, the Spatiotemporal Compressive Sensing method is adopted to predict it. Based on the predictors of two components, we can obtain a predictor of network traffic. From the simulation, the proposed prediction method outperforms three existing methods.

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“…The authors in Ref. [12] used the time series analysis to decompose the regular and random components, then used time series prediction to forecast the traffic patterns based on the regularity components, which exhibited high predictability. This work provides a novel approach at simplifying the time series data in wireless networks using time series analysis.…”

Section: Data Analyticsmentioning

confidence: 99%

Survey of wireless big data

Qian

Zhu

Zhang

2017

J. Commun. Inf. Netw.

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Wireless big data describes a wide range of massive data that is generated, collected and stored in wireless networks by wireless devices and users. While these data share some common properties with traditional big data, they have their own unique characteristics and provide numerous advantages for academic research and practical applications. This article reviews the recent advances and trends in the field of wireless big data. Due to space constraints, this survey is not intended to cover all aspects in this field, but to focus on the data aided transmission, data driven network optimization and novel applications. It is expected that the survey will help the readers to understand this exciting and emerging research field better. Moreover, open issues and promising future directions are also identified.

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Big Data Driven Mobile Traffic Understanding and Forecasting: A Time Series Approach

Cited by 197 publications

References 23 publications

Long-Term Mobile Traffic Forecasting Using Deep Spatio-Temporal Neural Networks

Long-Term Mobile Traffic Forecasting Using Deep Spatio-Temporal Neural Networks

Network Traffic Prediction Based on Deep Belief Network and Spatiotemporal Compressive Sensing in Wireless Mesh Backbone Networks

Survey of wireless big data

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