Statistical modeling of the Internet traffic dynamics: To which extent do we need long-term correlations?

Markelov, Oleg A.; Duc, Viet Nguyen; Bogachev, Mikhail I.

doi:10.1016/j.physa.2017.05.023

Cited by 31 publications

(18 citation statements)

References 48 publications

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“…We use the superstatistical approach for this purpose. One major trend for describing complex systems is to adopt the superstatistical approach, which recently becomes a perspective tool in physics, engineering, biology and other fields [23][24][25]. The basic idea of the superstatistical approach is to describe a considered complex system as a superposition of distributed statistics each of which represents a locally equilibrated subsystem.…”

Section: Modeling Of Waiting Time Distributionmentioning

confidence: 99%

Simulation of pseudo-text synthesis for generating words with long-range dynamic correlations

2020

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In a previous study, we have regarded real written texts as time series data and have tried to investigate dynamic correlations of word occurrences by utilizing an autocorrelation function (ACF). The results showed that the obtained ACFs can be classified into two groups: One was a group of ACFs showing dynamic correlations and the other was a group of ACFs showing no correlations. A word having the former ACF was called Type-I word, and a word with the latter ACF was called Type-II word. In this study, we investigate the mechanism of generating dynamic correlations of Type-I words by conducting simulations of pseudo-text synthesis. The results of the simulation clarified that inheritance of words between subsequent paragraphs is key to generate dynamic correlations of Type-I words, indicating a important role of long-range memory with duration time from few to several tens of sentences.

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Section: Modeling Of Waiting Time Distributionmentioning

confidence: 99%

Simulation of pseudo-text synthesis for generating words with long-range dynamic correlations

2020

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“…Apart from the analysis of internet traffic for network security reasons, internet data traffic carries a lot of useful information about the originating network. The daily volumetric variation of internet traffic creates usage pattern that can be deployed for predictive analysis which will help network engineers in preparing the network adequately for anticipated heavy internet traffic so as to ensure optimal quality of service [18][19][20]. Also, the quality of packet traffic may be impaired by packet losses [21][22][23][24].…”

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confidence: 99%

Data mining approach for predicting the daily Internet data traffic of a smart university

2019

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In this data era where data is the new oil, internet data traffic is growing significantly each year [1, 2]. With the advent of state of the art technologies on data transmission and processing in the last decade, the internet has witnessed an increase in the intensity and the volume of internet activities globally [3]. User-generated dataset contains useful statistics and information that can be harnessed for learning but this may be challenged by privacy issues [4, 5]. Internet activities generate data traffic of various kinds; during both data download and upload. Monitoring and analysis of internet traffic is becoming more challenging daily due to sheer increase in the volume of the internet data traffic and the large capacity of connection trunks [2]. Internet traffic measurement and management is vital to the operations of Internet Service Providers for predicting future demands [6], and traffic monitoring can be achieved using flow statistics tools. Internet traffic measurement is typically deployed

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“…When checked the long-term correlation of the air pollutants at different locations, we do believe the finite sample size and data quantization effects influence the results 32 . Factually, sample size and data quantization affect the results through several channels.…”

Section: Resultsmentioning

confidence: 97%

Forecasting extreme atmospheric events with a recurrence-interval-analysis-based autoregressive conditional duration model

Dai

Jiang

Zhou

2018

Sci Rep

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With most city dwellers in China subjected to air pollution, forecasting extreme air pollution spells is of paramount significance in both scheduling outdoor activities and ameliorating air pollution. In this paper, we integrate the autoregressive conditional duration model (ACD) with the recurrence interval analysis (RIA) and also extend the ACD model to a spatially autoregressive conditional duration (SACD) model by adding a spatially reviewed term to quantitatively explain and predict extreme air pollution recurrence intervals. Using the hourly data of six pollutants and the air quality index (AQI) during 2013–2016 collected from 12 national air quality monitoring stations in Beijing as our test samples, we attest that the spatially reviewed recurrence intervals have some general explanatory power over the recurrence intervals in the neighbouring air quality monitoring stations. We also conduct a one-step forecast using the RIA-ACD(1,1) and RIA-SACD(1,1,1) models and find that 90% of the predicted recurrence intervals are smaller than 72 hours, which justifies the predictive power of the proposed models. When applied to more time lags and neighbouring stations, the models are found to yield results that are consistent with reality, which evinces the feasibility of predicting extreme air pollution events through a recurrence-interval-analysis-based autoregressive conditional duration model. Moreover, the addition of a spatial term has proved effective in enhancing the predictive power.

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Statistical modeling of the Internet traffic dynamics: To which extent do we need long-term correlations?

Cited by 31 publications

References 48 publications

Simulation of pseudo-text synthesis for generating words with long-range dynamic correlations

Simulation of pseudo-text synthesis for generating words with long-range dynamic correlations

Data mining approach for predicting the daily Internet data traffic of a smart university

Forecasting extreme atmospheric events with a recurrence-interval-analysis-based autoregressive conditional duration model

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