Network Traffic Prediction and Result Analysis Based on Seasonal ARIMA and Correlation Coefficient

Yu, Yi; Wang, Jun; Song, Meina; Song, Junde

doi:10.1109/isdea.2010.335

Cited by 52 publications

(28 citation statements)

References 6 publications

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“…Then, the processes on the correlogram of EVN traffic stream show that the series needs to take the logarithm transformation (EVNLOG) and the 24-period seasonal difference (EVNLOGd0D1) to become variance stationary. Refer to the equation described in (2) we have:…”

Section: Resultsmentioning

confidence: 99%

A Multiplicative Seasonal ARIMA/GARCH Model in EVN Traffic Prediction

Beijing¹,

Hanoi²,

Vietnam³

2015

IJCNS

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This paper highlights the statistical procedure used in developing models that have the ability of capturing and forecasting the traffic of mobile communication network operating in Vietnam. To build such models, we follow Box-Jenkins method to construct a multiplicative seasonal ARIMA model to represent the mean component using the past values of traffic, then incorporate a GARCH model to represent its volatility. The traffic is collected from EVN Telecom mobile communication network. Diagnostic tests and examination of forecast accuracy measures indicate that the multiplicative seasonal ARIMA/GARCH model, i.e. ARIMA (1, 0, 1) × (0, 1, 1)24/GARCH (1, 1) shows a good estimation when dealing with volatility clustering in the data series. This model can be considered to be a flexible model to capture well the characteristics of EVN traffic series and give reasonable forecasting results. Moreover, in such situations that the volatility is not necessary to be taken into account, i.e. short-term prediction, the multiplicative seasonal ARIMA/GARCH model still acts well with the GARCH parameters adjusted to GARCH (0, 0).

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Section: Resultsmentioning

confidence: 99%

A Multiplicative Seasonal ARIMA/GARCH Model in EVN Traffic Prediction

Beijing¹,

Hanoi²,

Vietnam³

2015

IJCNS

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“…Zhang et al [1] propose an agile perception method to predict abnormal behavior. Yu et al [8] describe an ARIMA linear model to predict network flow sequence. Aiming at solving the problem that a single model cannot fully describe change characteristics, a wireless network flow prediction model based on combinatorial optimization theory is proposed by Chen and Liu [9].…”

Section: Related Workmentioning

confidence: 99%

A Peak Prediction Method for Subflow in Hybrid Data Flow

Zhang

Liu

Chen

et al. 2020

Scientific Programming

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Subflow prediction is required in resource active elastic scaling, but the existing single flow prediction methods cannot accurately predict the peak variation of subflow in hybrid data flow. These do not consider the correlation between subflows. The difficulty is that it is hard to calculate the correlation between different data flows in hybrid data flow. In order to solve this problem, this paper proposes a new method DCCSPP (subflow peak prediction of hybrid data flow based on delay correlation coefficients) to predict the peak value of hybrid data flow. Firstly, we establish a delay correlation coefficient model based on the sliding time window to determine the delay time and delay correlation coefficient. Next, based on the model, a hybrid data flow subflow peak prediction model and algorithm are established to achieve accurate peak prediction of subflow. Experiments show that our prediction model has achieved better results. Compared with LSTM, our method has decreased the MAE about 18.36% and RMSE 13.50%. Compared with linear regression, MAE and RMSE are decreased by 27.12% and 25.58%, respectively.

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“…ARIMA is a linear-based prediction model that is mainly used for predicting the future weather forecasting. Recently, this model has been also widely used for predicting network traffic, a nodes location, its future movement, remaining energy, and trust [31].…”

Section: Parameters Predictionmentioning

confidence: 99%

P-TCP: A prediction-based secure transmission control protocol for wireless Ad Hoc networks

Ghosh¹,

Datta²

2013

IETE J Res

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In this paper, we propose a prediction-based transmission control protocol (P-TCP) for wireless ad hoc networks that takes feedback from network layer to detect different types of packet losses and adjusts its parameters dynamically according to the varying network environment. Our proposed protocol predicts the values of the control parameters congestion window, congestion window limit, round trip time, and retransmission timeout from their previous state values. A security mechanism has been integrated with the protocol to make it usable in the highly vulnerable wireless ad hoc networks. A low complexity identity-based public key cryptography is proposed that generates a secret session key between source and destination nodes. The three-way handshaking process of P-TCP is secured using this secret session key. Performance comparison along with security analysis shows that the scheme is more efficient and secured compared to similar existing schemes. Simulation results show that P-TCP outperforms the popular New Reno and Ad hoc TCP protocols in terms of throughput and end-to-end delay in three standard network scenarios.

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Network Traffic Prediction and Result Analysis Based on Seasonal ARIMA and Correlation Coefficient

Cited by 52 publications

References 6 publications

A Multiplicative Seasonal ARIMA/GARCH Model in EVN Traffic Prediction

A Multiplicative Seasonal ARIMA/GARCH Model in EVN Traffic Prediction

A Peak Prediction Method for Subflow in Hybrid Data Flow

P-TCP: A prediction-based secure transmission control protocol for wireless Ad Hoc networks

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