40 research outputs found
Time series cluster kernels to exploit informative missingness and incomplete label information
The time series cluster kernel (TCK) provides a powerful tool for analysing multivariate time series subject to missing data. TCK is designed using an ensemble learning approach in which Bayesian mixture
models form the base models. Because of the Bayesian approach, TCK can naturally deal with missing
values without resorting to imputation and the ensemble strategy ensures robustness to hyperparameters, making it particularly well suited for unsupervised learning.
However, TCK assumes missing at random and that the underlying missingness mechanism is ignorable, i.e. uninformative, an assumption that does not hold in many real-world applications, such as e.g.
medicine. To overcome this limitation, we present a kernel capable of exploiting the potentially rich information in the missing values and patterns, as well as the information from the observed data. In our
approach, we create a representation of the missing pattern, which is incorporated into mixed mode mixture models in such a way that the information provided by the missing patterns is effectively exploited.
Moreover, we also propose a semi-supervised kernel, capable of taking advantage of incomplete label
information to learn more accurate similarities.
Experiments on benchmark data, as well as a real-world case study of patients described by longitudinal
electronic health record data who potentially suffer from hospital-acquired infections, demonstrate the
effectiveness of the proposed method
Time series kernel similarities for predicting Paroxysmal Atrial Fibrillation from ECGs
We tackle the problem of classifying Electrocardiography (ECG) signals with
the aim of predicting the onset of Paroxysmal Atrial Fibrillation (PAF). Atrial
fibrillation is the most common type of arrhythmia, but in many cases PAF
episodes are asymptomatic. Therefore, in order to help diagnosing PAF, it is
important to design procedures for detecting and, more importantly, predicting
PAF episodes. We propose a method for predicting PAF events whose first step
consists of a feature extraction procedure that represents each ECG as a
multi-variate time series. Successively, we design a classification framework
based on kernel similarities for multi-variate time series, capable of handling
missing data. We consider different approaches to perform classification in the
original space of the multi-variate time series and in an embedding space,
defined by the kernel similarity measure. We achieve a classification accuracy
comparable with state of the art methods, with the additional advantage of
detecting the PAF onset up to 15 minutes in advance
Time Series Cluster Kernel for Learning Similarities between Multivariate Time Series with Missing Data
Similarity-based approaches represent a promising direction for time series
analysis. However, many such methods rely on parameter tuning, and some have
shortcomings if the time series are multivariate (MTS), due to dependencies
between attributes, or the time series contain missing data. In this paper, we
address these challenges within the powerful context of kernel methods by
proposing the robust \emph{time series cluster kernel} (TCK). The approach
taken leverages the missing data handling properties of Gaussian mixture models
(GMM) augmented with informative prior distributions. An ensemble learning
approach is exploited to ensure robustness to parameters by combining the
clustering results of many GMM to form the final kernel.
We evaluate the TCK on synthetic and real data and compare to other
state-of-the-art techniques. The experimental results demonstrate that the TCK
is robust to parameter choices, provides competitive results for MTS without
missing data and outstanding results for missing data.Comment: 23 pages, 6 figure
Capturing Evolution Genes for Time Series Data
The modeling of time series is becoming increasingly critical in a wide
variety of applications. Overall, data evolves by following different patterns,
which are generally caused by different user behaviors. Given a time series, we
define the evolution gene to capture the latent user behaviors and to describe
how the behaviors lead to the generation of time series. In particular, we
propose a uniform framework that recognizes different evolution genes of
segments by learning a classifier, and adopt an adversarial generator to
implement the evolution gene by estimating the segments' distribution.
Experimental results based on a synthetic dataset and five real-world datasets
show that our approach can not only achieve a good prediction results (e.g.,
averagely +10.56% in terms of F1), but is also able to provide explanations of
the results.Comment: a preprint version. arXiv admin note: text overlap with
arXiv:1703.10155 by other author
HIVE-COTE: The hierarchical vote collective of transformation-based ensembles for time series classification:IEEE International Conference on Data Mining
There have been many new algorithms proposed over the last five years for solving time series classification (TSC) problems. A recent experimental comparison of the leading TSC algorithms has demonstrated that one approach is significantly more accurate than all others over 85 datasets. That approach, the Flat Collective of Transformation-based Ensembles (Flat-COTE), achieves superior accuracy through combining predictions of 35 individual classifiers built on four representations of the data into a flat hierarchy. Outside of TSC, deep learning approaches such as convolutional neural networks (CNN) have seen a recent surge in popularity and are now state of the art in many fields. An obvious question is whether CNNs could be equally transformative in the field of TSC. To test this, we implement a common CNN structure and compare performance to Flat-COTE and a recently proposed time series-specific CNN implementation.We find that Flat-COTE is significantly more accurate than both deep learning approaches on 85 datasets. These results are impressive, but Flat-COTE is not without deficiencies. We improve the collective by adding new components and proposing a modular hierarchical structure with a probabilistic voting scheme that allows us to encapsulate the classifiers built on each transformation. We add two new modules representing dictionary and interval-based classifiers, and significantly improve upon the existing frequency domain classifiers with a novel spectral ensemble. The resulting classifier, the Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE) is significantly more accurate than Flat-COTE and represents a new state of the art for TSC. HIVE-COTE captures more sources of possible discriminatory features in time series and has a more modular, intuitive structure
Automatic Feature Engineering for Time Series Classification: Evaluation and Discussion
Time Series Classification (TSC) has received much attention in the past two
decades and is still a crucial and challenging problem in data science and
knowledge engineering. Indeed, along with the increasing availability of time
series data, many TSC algorithms have been suggested by the research community
in the literature. Besides state-of-the-art methods based on similarity
measures, intervals, shapelets, dictionaries, deep learning methods or hybrid
ensemble methods, several tools for extracting unsupervised informative summary
statistics, aka features, from time series have been designed in the recent
years. Originally designed for descriptive analysis and visualization of time
series with informative and interpretable features, very few of these feature
engineering tools have been benchmarked for TSC problems and compared with
state-of-the-art TSC algorithms in terms of predictive performance. In this
article, we aim at filling this gap and propose a simple TSC process to
evaluate the potential predictive performance of the feature sets obtained with
existing feature engineering tools. Thus, we present an empirical study of 11
feature engineering tools branched with 9 supervised classifiers over 112 time
series data sets. The analysis of the results of more than 10000 learning
experiments indicate that feature-based methods perform as accurately as
current state-of-the-art TSC algorithms, and thus should rightfully be
considered further in the TSC literature