Change Point Detection of Sustainable Periods of Economic Systems Under the Robust Control

This article addresses the potential of mathematical and statistical modelling the change point detection in economic systems on the example of UC «RUSAL». Change point prediction of stable or quasi-stable periods of economic systems is necessary for the operational changing of a strategy, tactics and control of the considered economic system. It solves one of the robust control problems, the purpose of which is the synthesis of the regulator that can provide the preservation of output variables of the system within the robust limit for all types of membership functions and the uncertainty of the input data.The developed algorithm is based on the study of the behavior of residuals of regression models by the observed series of the dynamics of some exponent (as a benchmark was chosen the price of ordinary share). This algorithm is applicable for small volume samples, which, as a rule, are the series of dynamics of exponents of economic systems and also, in the study of non-Gaussian observational models.

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Robust control chart for change point detection of process variance in the presence of disturbances

10.1063/1.4907463 ◽

2015 ◽

Author(s):

Ng Kooi Huat ◽

Habshah Midi

Keyword(s):

Robust Control ◽

Control Chart ◽

Change Point ◽

Change Point Detection ◽

Process Variance ◽

Point Detection

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INTERVAL PIECEWISE REGRESSION MODEL WITH AUTOMATIC CHANGE-POINT DETECTION BY QUADRATIC PROGRAMMING

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems ◽

10.1142/s0218488505003503 ◽

2005 ◽

Vol 13 (03) ◽

pp. 347-361 ◽

Cited By ~ 5

Author(s):

JING-RUNG YU ◽

GWO-HSHIUNG TZENG ◽

HAN-LIN LI

Keyword(s):

Linear Programming ◽

Quadratic Programming ◽

Regression Model ◽

Change Point ◽

Regression Models ◽

Change Point Detection ◽

Fuzzy Regression ◽

Programming Approach ◽

Piecewise Regression ◽

Point Detection

To handle large variation data, an interval piecewise regression method with automatic change-point detection by quadratic programming is proposed as an alternative to Tanaka and Lee's method. Their unified quadratic programming approach can alleviate the phenomenon where some coefficients tend to become crisp in possibilistic regression by linear programming and also obtain the possibility and necessity models at one time. However, that method can not guarantee the existence of a necessity model if a proper regression model is not assumed especially with large variations in data. Using automatic change-point detection, the proposed method guarantees obtaining the necessity model with better measure of fitness by considering variability in data. Without piecewise terms in estimated model, the proposed method is the same as Tanaka and Lee's model. Therefore, the proposed method is an alternative method to handle data with the large variations, which not only reduces the number of crisp coefficients of the possibility model in linear programming, but also simultaneously obtains the fuzzy regression models, including possibility and necessity models with better fitness. Two examples are presented to demonstrate the proposed method.

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Sequential change point detection in linear quantile regression models

Statistics & Probability Letters ◽

10.1016/j.spl.2015.01.031 ◽

2015 ◽

Vol 100 ◽

pp. 98-103 ◽

Cited By ~ 7

Author(s):

Mi Zhou ◽

Huixia Judy Wang ◽

Yanlin Tang

Keyword(s):

Quantile Regression ◽

Change Point ◽

Regression Models ◽

Change Point Detection ◽

Sequential Change ◽

Point Detection

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Real-time particle filtering and smoothing algorithms for detecting abrupt changes in neural ensemble spike activity

Journal of Neurophysiology ◽

10.1152/jn.00684.2017 ◽

2018 ◽

Vol 119 (4) ◽

pp. 1394-1410 ◽

Cited By ~ 11

Author(s):

Sile Hu ◽

Qiaosheng Zhang ◽

Jing Wang ◽

Zhe Chen

Keyword(s):

Real Time ◽

Spike Activity ◽

Change Point ◽

Particle Filtering ◽

Jump Process ◽

Change Point Detection ◽

Stochastic Jump ◽

Abrupt Changes ◽

Non Gaussian ◽

Point Detection

Sequential change-point detection from time series data is a common problem in many neuroscience applications, such as seizure detection, anomaly detection, and pain detection. In our previous work (Chen Z, Zhang Q, Tong AP, Manders TR, Wang J. J Neural Eng 14: 036023, 2017), we developed a latent state-space model, known as the Poisson linear dynamical system, for detecting abrupt changes in neuronal ensemble spike activity. In online brain-machine interface (BMI) applications, a recursive filtering algorithm is used to track the changes in the latent variable. However, previous methods have been restricted to Gaussian dynamical noise and have used Gaussian approximation for the Poisson likelihood. To improve the detection speed, we introduce non-Gaussian dynamical noise for modeling a stochastic jump process in the latent state space. To efficiently estimate the state posterior that accommodates non-Gaussian noise and non-Gaussian likelihood, we propose particle filtering and smoothing algorithms for the change-point detection problem. To speed up the computation, we implement the proposed particle filtering algorithms using advanced graphics processing unit computing technology. We validate our algorithms, using both computer simulations and experimental data for acute pain detection. Finally, we discuss several important practical issues in the context of real-time closed-loop BMI applications. NEW & NOTEWORTHY Sequential change-point detection is an important problem in closed-loop neuroscience experiments. This study proposes novel sequential Monte Carlo methods to quickly detect the onset and offset of a stochastic jump process that drives the population spike activity. This new approach is robust with respect to spike sorting noise and varying levels of signal-to-noise ratio. The GPU implementation of the computational algorithm allows for parallel processing in real time.

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