10,646 research outputs found
The model of an anomaly detector for HiLumi LHC magnets based on Recurrent Neural Networks and adaptive quantization
This paper focuses on an examination of an applicability of Recurrent Neural
Network models for detecting anomalous behavior of the CERN superconducting
magnets. In order to conduct the experiments, the authors designed and
implemented an adaptive signal quantization algorithm and a custom GRU-based
detector and developed a method for the detector parameters selection. Three
different datasets were used for testing the detector. Two artificially
generated datasets were used to assess the raw performance of the system
whereas the 231 MB dataset composed of the signals acquired from HiLumi magnets
was intended for real-life experiments and model training. Several different
setups of the developed anomaly detection system were evaluated and compared
with state-of-the-art OC-SVM reference model operating on the same data. The
OC-SVM model was equipped with a rich set of feature extractors accounting for
a range of the input signal properties. It was determined in the course of the
experiments that the detector, along with its supporting design methodology,
reaches F1 equal or very close to 1 for almost all test sets. Due to the
profile of the data, the best_length setup of the detector turned out to
perform the best among all five tested configuration schemes of the detection
system. The quantization parameters have the biggest impact on the overall
performance of the detector with the best values of input/output grid equal to
16 and 8, respectively. The proposed solution of the detection significantly
outperformed OC-SVM-based detector in most of the cases, with much more stable
performance across all the datasets.Comment: Related to arXiv:1702.0083
Realtime market microstructure analysis: online Transaction Cost Analysis
Motivated by the practical challenge in monitoring the performance of a large
number of algorithmic trading orders, this paper provides a methodology that
leads to automatic discovery of the causes that lie behind a poor trading
performance. It also gives theoretical foundations to a generic framework for
real-time trading analysis. Academic literature provides different ways to
formalize these algorithms and show how optimal they can be from a
mean-variance, a stochastic control, an impulse control or a statistical
learning viewpoint. This paper is agnostic about the way the algorithm has been
built and provides a theoretical formalism to identify in real-time the market
conditions that influenced its efficiency or inefficiency. For a given set of
characteristics describing the market context, selected by a practitioner, we
first show how a set of additional derived explanatory factors, called anomaly
detectors, can be created for each market order. We then will present an online
methodology to quantify how this extended set of factors, at any given time,
predicts which of the orders are underperforming while calculating the
predictive power of this explanatory factor set. Armed with this information,
which we call influence analysis, we intend to empower the order monitoring
user to take appropriate action on any affected orders by re-calibrating the
trading algorithms working the order through new parameters, pausing their
execution or taking over more direct trading control. Also we intend that use
of this method in the post trade analysis of algorithms can be taken advantage
of to automatically adjust their trading action.Comment: 33 pages, 12 figure
A survey of outlier detection methodologies
Outlier detection has been used for centuries to detect and, where appropriate, remove anomalous observations from data. Outliers arise due to mechanical faults, changes in system behaviour, fraudulent behaviour, human error, instrument error or simply through natural deviations in populations. Their detection can identify system faults and fraud before they escalate with potentially catastrophic consequences. It can identify errors and remove their contaminating effect on the data set and as such to purify the data for processing. The original outlier detection methods were arbitrary but now, principled and systematic techniques are used, drawn from the full gamut of Computer Science and Statistics. In this paper, we introduce a survey of contemporary techniques for outlier detection. We identify their respective motivations and distinguish their advantages and disadvantages in a comparative review
Automated novelty detection in the WISE survey with one-class support vector machines
Wide-angle photometric surveys of previously uncharted sky areas or
wavelength regimes will always bring in unexpected sources whose existence and
properties cannot be easily predicted from earlier observations: novelties or
even anomalies. Such objects can be efficiently sought for with novelty
detection algorithms. Here we present an application of such a method, called
one-class support vector machines (OCSVM), to search for anomalous patterns
among sources preselected from the mid-infrared AllWISE catalogue covering the
whole sky. To create a model of expected data we train the algorithm on a set
of objects with spectroscopic identifications from the SDSS DR13 database,
present also in AllWISE. OCSVM detects as anomalous those sources whose
patterns - WISE photometric measurements in this case - are inconsistent with
the model. Among the detected anomalies we find artefacts, such as objects with
spurious photometry due to blending, but most importantly also real sources of
genuine astrophysical interest. Among the latter, OCSVM has identified a sample
of heavily reddened AGN/quasar candidates distributed uniformly over the sky
and in a large part absent from other WISE-based AGN catalogues. It also
allowed us to find a specific group of sources of mixed types, mostly stars and
compact galaxies. By combining the semi-supervised OCSVM algorithm with
standard classification methods it will be possible to improve the latter by
accounting for sources which are not present in the training sample but are
otherwise well-represented in the target set. Anomaly detection adds
flexibility to automated source separation procedures and helps verify the
reliability and representativeness of the training samples. It should be thus
considered as an essential step in supervised classification schemes to ensure
completeness and purity of produced catalogues.Comment: 14 pages, 15 figure
Detecting Outliers in Data with Correlated Measures
Advances in sensor technology have enabled the collection of large-scale
datasets. Such datasets can be extremely noisy and often contain a significant
amount of outliers that result from sensor malfunction or human operation
faults. In order to utilize such data for real-world applications, it is
critical to detect outliers so that models built from these datasets will not
be skewed by outliers.
In this paper, we propose a new outlier detection method that utilizes the
correlations in the data (e.g., taxi trip distance vs. trip time). Different
from existing outlier detection methods, we build a robust regression model
that explicitly models the outliers and detects outliers simultaneously with
the model fitting.
We validate our approach on real-world datasets against methods specifically
designed for each dataset as well as the state of the art outlier detectors.
Our outlier detection method achieves better performances, demonstrating the
robustness and generality of our method. Last, we report interesting case
studies on some outliers that result from atypical events.Comment: 10 page
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