Network traffic prediction based on seasonal arima model

Wang, Li; Li, Zengzhi; Song, Chengqian

doi:10.1109/wcica.2004.1340876

Cited by 14 publications

(8 citation statements)

References 3 publications

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“…In 2016, the seasonal autoregressive integrated moving average (SARIMA) model [34] was applied to predicting short-term photovoltaic generation in Greece, which was applicable to sequences with periodic variations [35]. In addition, Kusakci used the grey prediction model of the rolling mechanism (RM) to budget the annual net electricity consumption in Turkey [36].…”

Section: Overview Of Forecasting Methodsmentioning

confidence: 99%

Forecasting India’s Electricity Demand Using a Range of Probabilistic Methods

Zhou

2019

Energies

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With serious energy poverty, especially concerning power shortages, the economic development of India has been severely restricted. To some extent, power exploitation can effectively alleviate the shortage of energy in India. Thus, it is significant to balance the relationship between power supply and demand, and further stabilize the two in a reasonable scope. To achieve balance, a prediction of electricity generation in India is required. Thus, in this study, five methods, the metabolism grey model, autoregressive integrated moving average, metabolic grey model-auto regressive integrated moving average model, non-linear metabolic grey model and non-linear metabolic grey model-auto regressive integrated moving average model, are applied. We combine the characteristics of linear and nonlinear models, making a prediction and comparison of Indian power generation. In this way, we enrich methods for prediction research on electrical energy, which avoids large errors in trends of electricity generation due to those accidental factors when a single predictive model is used. In terms of prediction outcomes, the average relative errors from five models above are 1.67%, 1.62%, 0.84%, 1.84%, and 1.37%, respectively, which indicates high accuracy and reference value of these methods. In conclusion, India’s power generation will continue to grow with an average annual growth rate of 5.17% in the next five years (2018–2022).

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Section: Overview Of Forecasting Methodsmentioning

confidence: 99%

Forecasting India’s Electricity Demand Using a Range of Probabilistic Methods

Zhou

2019

Energies

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“…The model a4 is analyzed by ARIMA model, AIC (PACF) and partial autocorrelation function  4 a . The model is ARIMA (5,1,5). The ARIMA module of SPSS is obtained.…”

Section: Fitting Prediction Experiments (1) Wavelet Decomposition and Reconstructionmentioning

confidence: 99%

Study on the Prediction of Urban Traffic Flow Based on ARIMA Model

Lin¹

2017

dtetr

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Because the traffic flow has the characteristics of nonlinear and strong interference, good traffic model can accurately describe the flow characteristics, predict the changing trend of the flow and carry out the corresponding control measures to ensure the normal operation of traffic, thus improving the traffic pressure. In this paper, wavelet analysis is applied to analyze the complex network traffic. Time series are decomposed into approximate uncorrelated time series, and the time series are smoothed. The wavelet analysis is carried out by using the mathematical model, independent wavelet model and multi fractal wavelet model. Matlab and SPSS are used to analyze the data of traffic flow. The wavelet analysis and ARIMA model are used to predict the sequence of single branch reconstruction, which reduces the error and improves the feasibility.

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“…ARIMA has been discussed in [11,12] highlighting its use in modeling and prediction of network traffic. Authors in [4] discuss ARIMA modeling of traffic in an institutional wireless network.…”

Section: Related Workmentioning

confidence: 99%

“…ARIMA is a widely used statistical model for time series analysis and has also been used successfully in network traffic modeling [11,12]. Adaptive neuro fuzzy inference system (ANFIS) model [13] has been applied to forecast Internet traffic time series in [14].…”

Section: Introductionmentioning

confidence: 99%

Comparative evaluation of ARIMA and ANFIS for modeling of wireless network traffic time series

Yadav

Balakrishnan

2014

J Wireless Com Network

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Network traffic modeling significantly affects various considerations in networking, including network resource allocation, quality of service provisioning, network traffic management, congestion control, and bandwidth efficiency. These are very important issues in network protocol design, too. In this paper, a comprehensive comparison of modeling approaches of adaptive neuro fuzzy inference system (ANFIS) and autoregressive integrated moving average (ARIMA) for modeling of wireless network traffic in terms of typical statistical indicator and computational complexity has been attempted. ARIMA has been widely used in this area for past many years. On the other hand, ANFIS is comparatively new, and no network traffic modeling using ANFIS was attempted until recently to the best of our knowledge. At the same time, a detailed comparative performance evaluation of ANFIS with other modeling approaches in traffic modeling could not be found in existing literature. Reportedly, ANFIS provides a good precision in prediction in terms of statistical indicators and also gives effective description of network conditions at different times. However, the computational complexity of ANFIS for traffic modeling is a major concern and deserves a closer inspection. In our case of wireless network traffic, as a final result, we find that ANFIS model performs better than the best ARIMA model in three different scenarios.

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Network traffic prediction based on seasonal arima model

Cited by 14 publications

References 3 publications

Forecasting India’s Electricity Demand Using a Range of Probabilistic Methods

Forecasting India’s Electricity Demand Using a Range of Probabilistic Methods

Study on the Prediction of Urban Traffic Flow Based on ARIMA Model

Comparative evaluation of ARIMA and ANFIS for modeling of wireless network traffic time series

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