Trend forecasting based on Singular Spectrum Analysis of traffic workload in a large-scale wireless LAN

Abstract

a b s t r a c tNetwork traffic load in an IEEE802.11 infrastructure arises from the superposition of traffic accessed by wireless clients associated with access points (APs). An accurate load characterization can be beneficial in modeling network traffic and addressing a variety of problems including coverage planning, resource reservation and network monitoring for anomaly detection. This study focuses on the statistical analysis of the traffic load measured in a campus-wide IEEE802.11 infrastructure at each AP.Using the Singular Spectrum Analysis approach, we found that the time-series of traffic load at a given AP has a small intrinsic dimension. In particular, these time-series can be accurately modeled using a small number of leading (principal) components. This proved to be critical for understanding the main features of the components forming the network traffic.Statistical analysis of leading components has demonstrated that even a few first components form the main part of the information. The residual components capture the small irregular variations, which do not fit in the basic part of the network traffic and can be interpreted as a stochastic noise. Based on these properties, we also studied contributions of the various components to the overall structure of the traffic load of an AP and its variation over time.Finally, we designed and evaluated the performance of a traffic predictor for the trend component, obtained by projecting the original time-series on the set of leading components.

Moment tests for window length selection in singular spectrum analysis of short– and long–memory processes

“…In this, we are motivated by the fact that Khan and Poskitt (2010) have developed a description length principle that enables the user to consistently extract signal components (both theoretically and in practice) given a preassigned window length compatible with the Whitney embedding theorem. The techniques that we develop are related to the work of Tzagkarakis et al. (2009), who selected the window length as the point of first crossing of a confidence interval (CI) for the sample autocorrelation function (SACF).…”

“…(2009), who selected the window length as the point of first crossing of a confidence interval (CI) for the sample autocorrelation function (SACF). Although this might be appropriate for the type of data examined in Tzagkarakis et al. (2009), in general the point of first crossing of a (‘white noise’ 95%) CI by the SACF does not represent a time interval beyond which there is no memory left in the process.…”

Forecasting Home Sales in the Four Census Regions and the Aggregate US Economy Using Singular Spectrum Analysis

“…Based on Golyandina et al (2001) the window length should be chosen close to one-half of the time series length. More recently Khan and Poskitt (2011) have developed a minimum description length criterion which then followed by establishing numerical bounds on the mean squared reconstruction error by Tzagkarakis et al (2009). Based on that study, theoretical analysis of signal-noise separation and reconstruction in SSA can serve as a guide to optimal window choice.…”

Progressive optimization methods for applied in computer network

“…Výsledný model je možné rovněž testovat s použi-tím jiných typů neuronových sítí, případně lze optimalizaci přepínání řešit pomocí hybridních fuzzy modelů (Chang, B. R., 2009) nebo spektrální analýzy (Tzagkarakis, G., 2009 …”