Real-time Nature Of Data Research Articles

Modeling changes in US monetary policy with a time-varying nonlinear Taylor rule

Abstract The monetary economics literature has highlighted four issues that are important in evaluating US monetary policy since the late 1960s: (i) time variation in policy parameters, (ii) asymmetric preferences, (iii) real-time nature of data, and (iv) heteroskedasticity. In this paper, we exploit advances in sequential monte carlo methods to estimate a time-varying nonlinear Taylor rule that addresses these four issues simultaneously. Our findings suggest that US monetary policy has experienced substantial changes in terms of both the response to inflation and to real economic activity, as well as changes in preferences. These changes cannot be captured adequately by a single structural break at the late 1970s, as has been commonly assumed in the literature, and play a non-trivial role in economic performance.

End to End Encryption Architecture for Voice over Internet Protocol

Session Initiation Protocol (SIP) is an open standard protocol and constitutes the provisioning of services like Internet Telephony and Instant Messaging. Vulnerabilities in SIP have made it possible to exploit it and launch many of the known internet attacks and also few specific attacks, thus affecting the services deploying SIP for session management. To maintain the confidentiality and integrity of voice data security mechanisms need to be deployed. Currently available security measures do not take into account real time nature of data and are generic i.e. not optimized for VoIP technology. This paper presents new end to end encryption architecture for securing the VoIP calls which use SIP to establish their session taking into account the real time nature of data. General Terms SIP Vulnerability, VoIP Security, Threats and attacks, SIP Messages, SIP Communication

Real—Time Process Monitoring for Changing Inputs

A method is presented for statistical process control using real-time data in applications with finite processing times. This extends existing methods that monitor process behavior, assuming constant inputs and process conditions. Summary scores constructed from linear combinations of the realtime data are monitored both in real time and at the end of processing. The summary scores have a minimal loss of information due to the high dimensionality and correlated nature of real-time data. A multivariate regression model relates the inputs and initial conditions to the summary variables. This permits prediction of summary scores for different inputs and initial conditions. Large deviations from the predicted summary variables, measured by process monitor statistics, signal abnormal behavior. Diagnostic tools identify the patterns in the real-time variable(s) that cause the signals.

Real-Time Process Monitoring for Changing Inputs

A method is presented for statistical process control using real-time data in applications with finite processing times. This extends existing methods that monitor process behavior, assuming constant inputs and process conditions. Summary scores constructed from linear combinations of the realtime data are monitored both in real time and at the end of processing. The summary scores have a minimal loss of information due to the high dimensionality and correlated nature of real-time data. A multivariate regression model relates the inputs and initial conditions to the summary variables. This permits prediction of summary scores for different inputs and initial conditions. Large deviations from the predicted summary variables, measured by process monitor statistics, signal abnormal behavior. Diagnostic tools identify the patterns in the real-time variable(s) that cause the signals.