33,190 research outputs found
Generalization of Change-Point Detection in Time Series Data Based on Direct Density Ratio Estimation
The goal of the change-point detection is to discover changes of time series
distribution. One of the state of the art approaches of the change-point
detection are based on direct density ratio estimation. In this work we show
how existing algorithms can be generalized using various binary classification
and regression models. In particular, we show that the Gradient Boosting over
Decision Trees and Neural Networks can be used for this purpose. The algorithms
are tested on several synthetic and real-world datasets. The results show that
the proposed methods outperform classical RuLSIF algorithm. Discussion of cases
where the proposed algorithms have advantages over existing methods are also
provided
Poisson regression charts for the monitoring of surveillance time series
This paper presents a Poisson control chart for monitoring time series of counts typically arising in the surveillance of infectious diseases. The in-control mean is assumed to be time-varying and linear on the log-scale with intercept and seasonal components. If a shift in the intercept occurs the system goes out-of-control. Novel is that the magnitude of the shift does not have to be specified in advance: using the generalized likelihood ratio (GLR) statistic a monitoring scheme is formulated to detect on-line whether a shift in the intercept occurred. For this specific Poisson chart the necessary quantities of the GLR detector can be efficiently computed by recursive formulas. Extensions to more general Poisson charts e.g. containing an autoregressive epidemic component are discussed. Using Monte Carlo simulations run length properties of the proposed schemes are investigated. The practicability of the charts is demonstrated by applying them to the observed number of salmonella hadar cases in Germany 2001-2006
Change-Point Testing and Estimation for Risk Measures in Time Series
We investigate methods of change-point testing and confidence interval
construction for nonparametric estimators of expected shortfall and related
risk measures in weakly dependent time series. A key aspect of our work is the
ability to detect general multiple structural changes in the tails of time
series marginal distributions. Unlike extant approaches for detecting tail
structural changes using quantities such as tail index, our approach does not
require parametric modeling of the tail and detects more general changes in the
tail. Additionally, our methods are based on the recently introduced
self-normalization technique for time series, allowing for statistical analysis
without the issues of consistent standard error estimation. The theoretical
foundation for our methods are functional central limit theorems, which we
develop under weak assumptions. An empirical study of S&P 500 returns and US
30-Year Treasury bonds illustrates the practical use of our methods in
detecting and quantifying market instability via the tails of financial time
series during times of financial crisis
Speckle noise and dynamic range in coronagraphic images
This paper is concerned with the theoretical properties of high contrast
coronagraphic images in the context of exoplanet searches. We derive and
analyze the statistical properties of the residual starlight in coronagraphic
images, and describe the effect of a coronagraph on the speckle and photon
noise. Current observations with coronagraphic instruments have shown that the
main limitations to high contrast imaging are due to residual quasi-static
speckles. We tackle this problem in this paper, and propose a generalization of
our statistical model to include the description of static, quasi-static and
fast residual atmospheric speckles. The results provide insight into the
effects on the dynamic range of wavefront control, coronagraphy, active speckle
reduction, and differential speckle calibration. The study is focused on
ground-based imaging with extreme adaptive optics, but the approach is general
enough to be applicable to space, with different parameters.Comment: 31 pages, 18 figure
Detection of atrial fibrillation episodes in long-term heart rhythm signals using a support vector machine
Atrial fibrillation (AF) is a serious heart arrhythmia leading to a significant increase of the risk for occurrence of ischemic stroke. Clinically, the AF episode is recognized in an electrocardiogram. However, detection of asymptomatic AF, which requires a long-term monitoring, is more efficient when based on irregularity of beat-to-beat intervals estimated by the heart rate (HR) features. Automated classification of heartbeats into AF and non-AF by means of the Lagrangian Support Vector Machine has been proposed. The classifier input vector consisted of sixteen features, including four coefficients very sensitive to beat-to-beat heart changes, taken from the fetal heart rate analysis in perinatal medicine. Effectiveness of the proposed classifier has been verified on the MIT-BIH Atrial Fibrillation Database. Designing of the LSVM classifier using very large number of feature vectors requires extreme computational efforts. Therefore, an original approach has been proposed to determine a training set of the smallest possible size that still would guarantee a high quality of AF detection. It enables to obtain satisfactory results using only 1.39% of all heartbeats as the training data. Post-processing stage based on aggregation of classified heartbeats into AF episodes has been applied to provide more reliable information on patient risk. Results obtained during the testing phase showed the sensitivity of 98.94%, positive predictive value of 98.39%, and classification accuracy of 98.86%.Web of Science203art. no. 76
Signal Processing in Large Systems: a New Paradigm
For a long time, detection and parameter estimation methods for signal
processing have relied on asymptotic statistics as the number of
observations of a population grows large comparatively to the population size
, i.e. . Modern technological and societal advances now
demand the study of sometimes extremely large populations and simultaneously
require fast signal processing due to accelerated system dynamics. This results
in not-so-large practical ratios , sometimes even smaller than one. A
disruptive change in classical signal processing methods has therefore been
initiated in the past ten years, mostly spurred by the field of large
dimensional random matrix theory. The early works in random matrix theory for
signal processing applications are however scarce and highly technical. This
tutorial provides an accessible methodological introduction to the modern tools
of random matrix theory and to the signal processing methods derived from them,
with an emphasis on simple illustrative examples
An Incremental Construction of Deep Neuro Fuzzy System for Continual Learning of Non-stationary Data Streams
Existing FNNs are mostly developed under a shallow network configuration
having lower generalization power than those of deep structures. This paper
proposes a novel self-organizing deep FNN, namely DEVFNN. Fuzzy rules can be
automatically extracted from data streams or removed if they play limited role
during their lifespan. The structure of the network can be deepened on demand
by stacking additional layers using a drift detection method which not only
detects the covariate drift, variations of input space, but also accurately
identifies the real drift, dynamic changes of both feature space and target
space. DEVFNN is developed under the stacked generalization principle via the
feature augmentation concept where a recently developed algorithm, namely
gClass, drives the hidden layer. It is equipped by an automatic feature
selection method which controls activation and deactivation of input attributes
to induce varying subsets of input features. A deep network simplification
procedure is put forward using the concept of hidden layer merging to prevent
uncontrollable growth of dimensionality of input space due to the nature of
feature augmentation approach in building a deep network structure. DEVFNN
works in the sample-wise fashion and is compatible for data stream
applications. The efficacy of DEVFNN has been thoroughly evaluated using seven
datasets with non-stationary properties under the prequential test-then-train
protocol. It has been compared with four popular continual learning algorithms
and its shallow counterpart where DEVFNN demonstrates improvement of
classification accuracy. Moreover, it is also shown that the concept drift
detection method is an effective tool to control the depth of network structure
while the hidden layer merging scenario is capable of simplifying the network
complexity of a deep network with negligible compromise of generalization
performance.Comment: This paper has been published in IEEE Transactions on Fuzzy System
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