Corpus ID: 56226230

Change Points Detection of Vector Autoregressive Model using SDVAR Algorithm

@inproceedings{Saaid2012ChangePD,
  title={Change Points Detection of Vector Autoregressive Model using SDVAR Algorithm},
  author={Fatimah Almah Saaid and Darfiana Nur and Robert King},
  year={2012}
}
Part of a larger research project to detect fraudulent acts using the telecommunications call details record (CDR) is to locate the change points which could lead to detecting suspicious (fraudulent) calls. The capability of sequential discounting for autoregressive (SDAR) model learning algorithm (as proposed by [6]) to detect change points in time series data is explored. The algorithm is extended to multivariate time series by employing vector autoregressive model using SDVAR. Simulation and… Expand
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References

SHOWING 1-10 OF 12 REFERENCES
A unifying framework for detecting outliers and change points from time series
TLDR
This paper presents a unifying framework for dealing with outlier detection and change point detection, which is incrementally learned using an online discounting learning algorithm and compared with conventional methods to demonstrate its validity through simulation and experimental applications to incidents detection in network security. Expand
Change point detection based on call detail records
TLDR
A method for combining wavelet denoising and sequential approach for detecting change points on mobile phone based on detailed call records and achieves good performance with high accuracy is proposed. Expand
Change-Point Detection in Time-Series Data Based on Subspace Identification
TLDR
A batch-type algorithm applicable to ordinary time-series data, i.e. consisting of only output series, is derived and then the online version of the algorithm and the extension to be available with input-output time- series data are introduced. Expand
Introduction to Multiple Time Series Analysis
Introduction to multiple time series analysis
TLDR
The choice of point and interval forecasts as well as innovation accounting are presented as tools for structural analysis within the multiple time series context. Expand
Development of Users' Call Profiles using Unsupervised Random Forest
The aim of this paper is to detect fraud in telecommunications data which consists of millions of call records generated each day. The fraud detection is implemented via the construction of user callExpand
Time series analysis - univariate and multivariate methods
TLDR
This work presents a meta-modelling framework for estimating the modeled properties of the Shannon filter, which automates the very labor-intensive and therefore time-heavy process of Fourier analysis. Expand
Online data mining for co-evolving time sequences
TLDR
This work develops a fast method to analyze co-evolving time sequences jointly to allow estimation/forecasting of missing/delayed/future values, quantitative data mining, and outlier detection, and adapts to changing correlations among time sequences. Expand
Bayesian Change-Point Analysis of Tropical Cyclone Activity: The Central North Pacific Case*
Bayesian analysis is applied to detect change points in the time series of annual tropical cyclone counts over the central North Pacific. Specifically, a hierarchical Bayesian approach involvingExpand
A fast Bayesian change point analysis for the segmentation of microarray data
TLDR
A new implementation of the Bayesian change point method that is O(n) in both speed and memory is presented; bcp 2.1 runs in approximately 45 s on a single processor with a sequence of length 10,000--a tremendous speed gain. Expand
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