15,417 research outputs found
Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles.
With the sweeping digitalization of societal, medical, industrial, and
scientific processes, sensing technologies are being deployed that produce
increasing volumes of time series data, thus fueling a plethora of new or
improved applications. In this setting, outlier detection is frequently
important, and while solutions based on neural networks exist, they leave room
for improvement in terms of both accuracy and efficiency. With the objective of
achieving such improvements, we propose a diversity-driven, convolutional
ensemble. To improve accuracy, the ensemble employs multiple basic outlier
detection models built on convolutional sequence-to-sequence autoencoders that
can capture temporal dependencies in time series. Further, a novel
diversity-driven training method maintains diversity among the basic models,
with the aim of improving the ensemble's accuracy. To improve efficiency, the
approach enables a high degree of parallelism during training. In addition, it
is able to transfer some model parameters from one basic model to another,
which reduces training time. We report on extensive experiments using
real-world multivariate time series that offer insight into the design choices
underlying the new approach and offer evidence that it is capable of improved
accuracy and efficiency. This is an extended version of "Unsupervised Time
Series Outlier Detection with Diversity-Driven Convolutional Ensembles", to
appear in PVLDB 2022.Comment: 14 pages. An extended version of "Unsupervised Time Series Outlier
Detection with Diversity-Driven Convolutional Ensembles", to appear in PVLDB
202
Time-Series Data Mining:A Review
Data mining refers to the extraction of knowledge by analyzing the data from different perspectives and accumulates them to form useful information which could help the decision makers to take appropriate decisions. Classification and clustering has been the two broad areas in data mining. As the classification is a supervised learning approach, the clustering is an unsupervised learning approach and hence can be performed without the supervision of the domain experts. The basic concept is to group the objects in such a way so that the similar objects are closer to each. Time series data is observation of the data over a period of time. The estimation of the parameter, outlier detection and transformation of the data are some ofthe basic issues in handling the time series data. An approach is given for clustering the data based on the membership values assigned to each data point compressing the effect of outlier or noise present in the data. The Possibilistic Fuzzy C-Means (PFCM) with Error Prediction (EP) are done for the clustering and noise identification in the time-series data
Robust archetypoids for anomaly detection in big functional data
Archetypoid analysis (ADA) has proven to be a successful unsupervised statistical technique to identify extreme observations in the periphery of the data cloud, both in classical multivariate data and functional data. However, two questions remain open in this field: the use of ADA for outlier detection and its scalability. We propose to use robust functional archetypoids and adjusted boxplot to pinpoint functional outliers. Furthermore, we present a new archetypoid algorithm for obtaining results from large data sets in reasonable time. Functional time series are occurring in many practical problems, so this paper focuses on functional data settings. The new algorithm for detecting functional anomalies, called CRO-FADALARA, can be used with both univariate and multivariate curves. Our proposal for outlier detection is compared with all the state-of-the-art methods in a controlled study, showing a good performance. Furthermore, CRO-FADALARA is applied to two large time series data sets, where outliers curves are discussed and the reduction in computational time is clearly stated. A third case study with a small ECG data set is discussed, given its importance in functional data scenarios. All data, R code and a new R package are freely available
A taxonomy framework for unsupervised outlier detection techniques for multi-type data sets
The term "outlier" can generally be defined as an observation that is significantly different from
the other values in a data set. The outliers may be instances of error or indicate events. The
task of outlier detection aims at identifying such outliers in order to improve the analysis of
data and further discover interesting and useful knowledge about unusual events within numerous
applications domains. In this paper, we report on contemporary unsupervised outlier detection
techniques for multiple types of data sets and provide a comprehensive taxonomy framework and
two decision trees to select the most suitable technique based on data set. Furthermore, we
highlight the advantages, disadvantages and performance issues of each class of outlier detection
techniques under this taxonomy framework
A systematic review of data quality issues in knowledge discovery tasks
Hay un gran crecimiento en el volumen de datos porque las organizaciones capturan permanentemente la cantidad colectiva de datos para lograr un mejor proceso de toma de decisiones. El desafío mas fundamental es la exploración de los grandes volúmenes de datos y la extracción de conocimiento útil para futuras acciones por medio de tareas para el descubrimiento del conocimiento; sin embargo, muchos datos presentan mala calidad. Presentamos una revisión sistemática de los asuntos de calidad de datos en las áreas del descubrimiento de conocimiento y un estudio de caso aplicado a la enfermedad agrícola conocida como la roya del café.Large volume of data is growing because the organizations are continuously capturing the collective amount of data for better decision-making process. The most fundamental challenge is to explore the large volumes of data and extract useful knowledge for future actions through knowledge discovery tasks, nevertheless many data has poor quality. We presented a systematic review of the data quality issues in knowledge discovery tasks and a case study applied to agricultural disease named coffee rust
Autoencoders for strategic decision support
In the majority of executive domains, a notion of normality is involved in
most strategic decisions. However, few data-driven tools that support strategic
decision-making are available. We introduce and extend the use of autoencoders
to provide strategically relevant granular feedback. A first experiment
indicates that experts are inconsistent in their decision making, highlighting
the need for strategic decision support. Furthermore, using two large
industry-provided human resources datasets, the proposed solution is evaluated
in terms of ranking accuracy, synergy with human experts, and dimension-level
feedback. This three-point scheme is validated using (a) synthetic data, (b)
the perspective of data quality, (c) blind expert validation, and (d)
transparent expert evaluation. Our study confirms several principal weaknesses
of human decision-making and stresses the importance of synergy between a model
and humans. Moreover, unsupervised learning and in particular the autoencoder
are shown to be valuable tools for strategic decision-making
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