Event Characterization and Prediction Based on Temporal Patterns in Dynamic Data System - 2014


The new method proposed in this paper applies a multivariate reconstructed phase space (MRPS) for identifying multivariate temporal patterns that are characteristic and predictive of anomalies or events in a dynamic data system. The new method extends the original univariate reconstructed phase space framework, which is based on fuzzy unsupervised clustering method, by incorporating a new mechanism of data categorization based on the definition of events. In addition to modeling temporal dynamics in a multivariate phase space, a Bayesian approach is applied to model the first-order Markov behavior in the multidimensional data sequences. The method utilizes an exponential loss objective function to optimize a hybrid classifier which consists of a radial basis kernel function and a log-odds ratio component. We performed experimental evaluation on three data sets to demonstrate the feasibility and effectiveness of the proposed approach.

Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here

PROJECT TITLE : Event Popularity Prediction Using Influential Hashtags from Social Media ABSTRACT: The estimation of the scope of information propagation, the making of decisions, and the prevention of emergencies all require
PROJECT TITLE : ES2: Building an Efficient and Responsive Event Path for I/O Virtualization ABSTRACT: I/O virtualization's primary performance bottleneck is hypervisor intervention in the virtual I/O event path. This is due to
PROJECT TITLE : Preference and Constraint Factor Model for Event Recommendation ABSTRACT: The recently established Event-based Social Network, also known as EBSN, is focused on bridging the gap between offline social gatherings
PROJECT TITLE : Learning Fuzzy Automaton’s Event Transition Matrix When Post-Event State Is Unknown ABSTRACT: When compared to other techniques for system modeling, the fuzzy discrete event systems (FDESs) methodology has the
PROJECT TITLE : Discovering Temporal Patterns for Event Sequence Clustering via Policy Mixture Model ABSTRACT: The Temporal Point Process, or TPP for short, is an expressive modeling tool that can be used to analyze the temporal

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry