Traditional statistical analysis of network data is often employed to determine traffic distribution, to summarize user’s behavior patterns, or to predict future network traffic. Mining of network data may be used to discover hidden user groups, to detect payment fraud, or to identify network abnormalities. In our research we combine traditional traffic analysis with data mining technique. We a...

The use of IP traffic prediction techniques for DAMA TDMA protocols is investigated in this paper. The predicted traffic distribution is derived when the input traffic shows long-range dependence features. Furthermore, an equivalent bandwidth is calculated, which allows the wireless terminal to request a certain amount of bandwidth (slot duration) in terms of a target traffic loss probability. ...

This paper presents a prediction algorithm for estimating the upper bound of future Web traffic volume. Unlike traditional traffic predictions that are performed at a single time scale using curve fitting, we employ a multiple time scale approach and utilize traffic statistical properties to do forecasting. We have applied our prediction algorithm to the 1998 World Cup data set. Experiments sho...

Measurements of high-speed network traffic have shown that traffic data exhibits a high degree of self-similarity. Traditional traffic models such as AR and ARMA are not able to capture this long-range-dependence making them ineffective for the traffic prediction task. In this paper, we apply the fractional ARIMA (F-ARIMA) model to predict one-step-ahead traffic value at different time scales. ...

Spatial-temporal prediction has many applications such as climate forecasting and urban planning. In particular, traffic prediction has drawn increasing attention in data mining research field for the growing traffic related datasets and for its impacts in real-world applications. For example, an accurate taxi demand prediction can assist taxi companies to pre-allocate taxis to meet with commut...

Background and Aim: High concentrations of particulate matter-25 (PM2.5) have been the cause of the unhealthiest days in Tehran, Iran in recent years. This study was conducted with the aim of the spatio-temporal analysis of traffic volume and its relationship with PM2.5 pollutant concentrations in Tehran metropolis, Tehran during 2015-2018, using the Geographic Information System (GIS). Materi...

Inspired by an existing linear dynamic system air traffic model, a discrete-time Link Transmission Model is developed for air traffic flow prediction and optimization in this paper. Compared with a Large-capacity Cell Transmission Model, this model reduces the number of state variables by approximately an order of 10, which decreases the computing time in prediction and optimization of air traf...

Because network traffic is complex and the existing prediction models have various limitations, a new network traffic prediction model based on wavelet transform and optimized support vector machine(ChOSVM) is proposed. Firstly, the network traffic is decomposed to the scale coefficients and wavelet coefficients by non-decimated wavelet transform based on suitable wavelet base and decomposition...

Traffic conditions vary over time, and therefore, traffic behavior should be modeled as a stochastic process. In this study, a probabilistic approach utilizing Autocorrelation is proposed to model the stochastic variation of traffic conditions, and subsequently, predict the traffic conditions. Using autocorrelation of the time series samples of density and flow which are collected from segments...