Security problems with network are significant, such as network failures and malicious attacks. Monitoring network traffic and detect anomalies of network traffic is one of the effective manner to ensure network security. In this paper, we propose a hybrid method for network traffic prediction and anomaly detection. Specifically, the original network traffic data is decomposed into high-frequen...

This paper presents analysis of a modified Feed Forward Multilayer Perceptron (FMP) by inserting an ARMA (Auto Regressive Moving Average) model at each neuron (processor node) with the Backp ropagation learning algorithm. The stability analysis is presented to establish the convergence theory of the Back propagation algorithm based on the Lyapunov function. Furthermore, the analysis extends the...

During this field survey, we measured and recorded a few quality parameters of wireless communication in a substation switchyard. A microprocessor-based measurement system was used for data collection and analysis. We investigated long-term noise variation in this specific environment. Based on our measurement and post-processing analysis we conclude that the so-called Classic/Bayesian assumpti...

In this paper an alternative adaptation of the Orthogonal Block Adaptive Line Enhancer (OBALE) is presented. The adaptive lter within the OBALE has an auto-regressive-moving-average (ARMA) form which is based on classical Laguerre orthogonal functions. This has the advantages of short lter length and, with a lattice structure, stability. The frequencies and radii of the poles within the orthogo...

In this paper, a modified estimation algorithm has been developed refers to Covariance Shaping Least Square (CSLS) estimation based on the quantum mechanical concepts and constraints. The algorithm has been applied to Auto Regressive Moving Average (ARMA models with various parameter values. The same models can be applied with Colored Noise which estimates the bias in the parameter and the vali...

This paper presents an approach for automatic classification of pulsed Terahertz (THz), or T-ray, signals highlighting their potential in biomedical, pharmaceutical and security applications. T-ray classification systems supply a wealth of information about test samples and make possible the discrimination of heterogeneous layers within an object. In this paper, a novel technique involving the ...

Auto-regressive (AR) time series (TS) models are useful for structural damage detection in vibration-based health monitoring (SHM). However, certain limitations, e.g., non-stationarity and subjective feature selection, have reduced its wide-spread use. With increasing trends machine learning (ML) technologies, automated recognition is becoming popular attracting many researchers. In this paper,...

This is an early version of a set of notes for the course on Time Series Analysis offered at Lund University. Any and all comments and suggestions are most welcome and appreciated. Glossary and Abbreviations 1-D, 2-D, etc. One-dimensional, two-dimensional, etc. ACF Auto-covariance function AR Autoregressive ARIMA Integrated ARMA ARMA Autoregressive moving average ARMAX ARMA with exogenous input...

This study compared the methods used to forecast increases in power consumption caused by rising popularity of electric vehicles (EVs). An excellent model for each region was proposed using multiple scaled geographical datasets over two years. EV charging volumes are influenced various factors, including condition a vehicle, battery’s state-of-charge (SOC), and distance destination. However, su...