This work proposes a new exchangeability test for random sequence through martingale-based approach. Its main contributions include 1) an additive martingale which is more amenable designing tests by exploiting the Hoeffding-Azuma lemma and 2) different betting functions constructing martingale. By choosing underlying probability density function of p-values as function, it can be shown that, w...

We consider the problem of quickest change-point detection in data streams. Classical change-point detection procedures, such as CUSUM, Shiryaev-Roberts and Posterior Probability statistics, are optimal only if the change-point model is known, which is an unrealistic assumption in typical applied problems. Instead we propose a new method for change-point detection based on Inductive Conformal M...

Since the work of Page in the 1950s, the problem of detecting an abrupt change in the distribution of stochastic processes has received a great deal of attention. In particular, a deep connection has been established between Lorden’s minimax approach to change-point detection and the widely used CUSUM procedure, first for discrete-time processes, and subsequently for some of their continuous-ti...

The contribution is focused on change point detection in a one-dimensional stochastic process by sparse parameter estimation from an overparametrized model. A stochastic process with change in the mean is estimated using dictionary consisting of Heaviside functions. The basis pursuit algorithm is used to get sparse parameter estimates. The mentioned method of change point detection in a stochas...

We propose a change point detection algorithm for a sequence of graphs. Our algorithm focuses on the change of the structure of densely connected subgraphs (community structure) rather than the change of the link weights. In contrast to the traditional approaches, the algorithm can identify the structure change more sensitively. Experiments with a synthetic data and a real-world data of graphs ...

This work presents a novel approach to detect a change in the state of a signal. We propose that in a given state, the values of a signal vary in a subrange of a Gaussian distribution. We describe methods to monitor a signal in real time for change points based upon sub-Gaussian fitting. The proposed algorithm was implemented and tested on heartrate variability data.

We study the problem of detecting a change in the mean of one-dimensional Gaussian process data. This problem is investigated in the setting of increasing domain (customarily employed in time series analysis) and in the setting of fixed domain (typically arising in spatial data analysis). We propose a detection method based on the generalized likelihood ratio test (GLRT), and show that our meth...

The contribution deals with use of overcomplete models and sparse parameter estimation for change point detection in one–dimensional stochastic processes. These processes are estimated by ’Heaviside’ functions. The BASIS PURSUIT algorithm is used to get sparse parameter estimation. The mentioned method of change point detection in stochastic processes is compared with standard methods by simula...

Change-point detection has been a fundamental problem in quality control, hazards monitoring and cybersecurity. With the ever-growing complexity of system and enlarging number of sensors, multi-sensor detection algorithms are in real demand. However, most existing detection algorithms consider a centralized approach, meaning that one collects all data at one location, which sometimes may be inf...