Shapelet Candidates Research Articles

Short isometric shapelet transform for binary time series classification

In the research area of time series classification, the ensemble shapelet transform algorithm is one of state-of-the-art algorithms for classification. However, its high time complexity is an issue to hinder its application since its base classifier shapelet transform includes a high time complexity of a distance calculation and shapelet selection. Therefore, in this paper we introduce a novel algorithm, i.e. short isometric shapelet transform, which contains two strategies to reduce the time complexity. The first strategy of SIST fixes the length of shapelet based on a simplified distance calculation, which largely reduces the number of shapelet candidates as well as speeds up the distance calculation in the ensemble shapelet transform algorithm. The second strategy is to train a single linear classifier in the feature space instead of an ensemble classifier. The theoretical evidences of these two strategies are presented to guarantee a near-lossless accuracy under some preconditions while reducing the time complexity. Furthermore, empirical experiments demonstrate the superior performance of the proposed algorithm.

ShapeNet: A Shapelet-Neural Network Approach for Multivariate Time Series Classification

Time series shapelets are short discriminative subsequences that recently have been found not only to be accurate but also interpretable for the classification problem of univariate time series (UTS). However, existing work on shapelets selection cannot be applied to multivariate time series classification (MTSC) since the candidate shapelets of MTSC may come from different variables of different lengths and thus cannot be directly compared. To address this challenge, in this paper, we propose a novel model called ShapeNet, which embeds shapelet candidates of different lengths into a unified space for shapelet selection. The network is trained using cluster-wise triplet loss, which considers the distance between anchor and multiple positive (negative) samples and the distance between positive (negative) samples, which are important for convergence. We compute representative and diversified final shapelets rather than directly using all the embeddings for model building to avoid a large fraction of non-discriminative shapelet candidates. We have conducted experiments on ShapeNet with competitive state-of-the-art and benchmark methods using UEA MTS datasets. The results show that the accuracy of ShapeNet is the best of all the methods compared. Furthermore, we illustrate the shapelets’ interpretability with two case studies.

An improved fast shapelet selection algorithm and its application to pervasive EEG

With the rapid development of pervasive devices, a great deal of time series are generated by various sensors, and many time series classification (TSC) algorithms have been proposed to deal with these data. Among them, shapelet-based algorithms have attracted great attention due to its high accuracy and strong interpretability. However, time complexity of shapelet-based algorithms is high. In this paper, we propose an improved Fast Shapelet Selection algorithm based on Clustering (FSSoC), which greatly reduces the time of shapelet selection. Firstly, time series are clustered into several groups with improved k-means, and then some time series are sampled from each cluster with a strategy based on Euclidean Distance sorting. Secondly, Important Data Points (IDPs) of the sampled time series are identified and only the subsequences between two nonadjacent IDPs are added to shapelet candidates. Therefore, the number of shapelet candidates is greatly reduced, which leads to a obviously reduction in time consumption. Thirdly, FSSoC is applied to shapelet transformation algorithm to test classification accuracy and running time, the experiments demonstrate that FSSoC is obviously faster than existing shapelet selection algorithms while keeping a high accuracy. At last, a case study on EEG time series is presented, which verifies the feasibility of FSSoC application to automatically discover representative EEG features.

ELIS++: a shapelet learning approach for accurate and efficient time series classification

In recent years, time series classification with shapelets, due to the high accuracy and good interpretability, has attracted considerable interests. These approaches extract or learn shapelets from the training time series. Although they can achieve higher accuracy than other approaches, there still confront some challenges. First, they may suffer from low accuracy in the case of small training dataset. Second, they must manually set some parameters, like the number of shapelets and the length of each shapelet beforehand, and some hyper-parameters, like learning rate and regulation weight, which are difficult to set without prior knowledge. Third, extracting or learning shapelets incurs a huge computation cost, due to the huge search space. In this paper, we extend our previous shapelet learning approach ELIS to ELIS++. To improve the accuracy on the small training dataset, we propose a data augmentation approach. To learn the higher quality shapelets, based on the PAA shapelet candidates search technique proposed in ELIS, ELIS++ first propose a novel entropy-based approach shapelet candidate selection mechanism to discover shapelet candidates, and then applies the logistic regression model to adjust shapelets.To avoid setting other parameters manually, we propose a Bayesian Optimization based approach. Moreover, two techniques are proposed to improve the efficiency, coarse-grained shapelet adjustment and SIMD-based parallel computation. We conduct extensive experiments on 35 UCR datasets, and results verify the effectiveness and efficiency of ELIS++.

An Effective Algorithm for Intrusion Detection Using Random Shapelet Forest

Detection of abnormal network traffic is an important issue when builds intrusion detection systems. An effective way to address this issue is time series mining, in which the network traffic is naturally represented as a set of time series. In this paper, we propose a novel efficient algorithm, called RSFID (Random Shapelet Forest for Intrusion Detection), to detect abnormal traffic flow patterns in periodic network packets. Firstly, the Fast Correlation‐based Filter (FCBF) algorithm is employed to remove irrelevant features to decrease the overfitting as well as the time complexity. Then, a random forest which is built upon a set of shapelet candidates is used to classify the normal and abnormal traffic flow patterns. Specifically, the Symbolic Aggregate approXimation (SAX) and random sampling technique are adopted to mitigate the high time complexity caused by enumerating shapelet candidates. Experimental results show the effectiveness and efficiency of the proposed algorithm.

Efficient Shapelet Discovery for Time Series Classification

Time-series shapelets are discriminative subsequences, recently found effective for time series classification ( <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tsc</small> ). It is evident that the quality of shapelets is crucial to the accuracy of <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tsc</small> . However, major research has focused on building accurate models from some shapelet candidates. To determine such candidates, existing studies are surprisingly simple, e.g., enumerating subsequences of some fixed lengths, or randomly selecting some subsequences as shapelet candidates. The major bulk of computation is then on building the model from the candidates. In this paper, we propose a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">efficient shapelet discovery</i> method, called <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bspcover</small> , to discover a set of high-quality shapelet candidates for model building. Specifically, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bspcover</small> generates abundant candidates via Symbolic Aggregate approXimation with sliding window, then prunes identical and highly similar candidates via <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Bloom filters</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">similarity matching</i> , respectively. We next propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><inline-formula><tex-math notation="LaTeX">$p$</tex-math><alternatives><mml:math><mml:mi>p</mml:mi></mml:math><inline-graphic xlink:href="li-ieq1-2995870.gif"/></alternatives></inline-formula>-Cover algorithm</i> to efficiently determine discriminative shapelet candidates that maximally represent each time-series class. Finally, any existing shapelet learning method can be adopted to build a classification model. We have conducted extensive experiments with well-known time-series datasets and representative state-of-the-art methods. Results show that <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bspcover</small> speeds up the state-of-the-art methods by more than 70 times, and the accuracy is often comparable to or higher than existing works.

Extracting diverse-shapelets for early classification on time series

In recent years, early classification on time series has become increasingly important in time-sensitive applications. Existing shapelet based methods still cannot work well on this problem. First, the effectiveness of traditional shapelet based methods would be influenced by the number of shapelet candidates. Second, it is difficult for previous methods to obtain diverse shapelets in shapelet selection. In this paper, we propose an Improved Early Distinctive Shapelet Classification method named IEDSC. We first present a new method to more precisely measure the similarity between time series, which takes into account of the relative trend of time series. Second, in shapelet extraction, we propose a pruning technique to reduce the number of shapelets by predicting the starting positions of shapelets with good quality. In addition, a new shapelet selection method is also proposed to remove the similar shapelets, so as to maintain the diversity of shapelets. Finally, the experimental results on 16 benchmark datasets show that the proposed method outperforms state-of-the-art for early classification on time series.

Discovering shapelets with key points in time series classification

Shapelet is a time series subsequence that can best represent the time series of one class. Shapelet can improve the accuracy and efficiency of classification, as well as the interpretability of classification results. Although shapelet has good classification performance, how to efficiently find the optimal shapelet is still an important challenge due to the large number of shapelet candidates contained in a time series. In this paper, a new shapelet discovery method, referred to as Pruning Shapelets with Key Points (PSKP), is proposed. PSKP first finds the key points in time series according to the standard deviation of each time tick of time series, and then extracts shapelet candidates with these key points. Finally, PSKP classifies the time series through a decision tree constructed based on the optimal shapelet. We make experiments on various data sets and evaluate the performance of the proposed method with compared candidates. The experimental results demonstrate that the proposed method is feasible and effective.

A just-in-time shapelet selection service for online time series classification

Time series classification attracted significant interest over the past decade as a result of the enormous data which can be inserted into the Cyber-Physical System. However, in such system time variant mechanisms violate the stationarity hypothesis which is mostly assumed in the design of classification systems, hence this impairs the accuracy of the classifier. In order to cope with this issue, classifiers with just-in-time adaptive training mechanisms are needed, as they allow detecting a change in stationarity and modifying the classifier configuration accordingly to track the process evolution. This paper proposes an online time series classification system including a just-in-time shapelet selection service (JSSS) which selects shapelets as the features for time series classification. The JSSS is based on a fast shapelet selection algorithm (FSS). First, the FSS samples some time series from training dataset with the help of the subclass splitting method. Next, the FSS identifies Local Farthest Deviation Points (LFDPs) from sampled time series; then, the subsequences between two different LFDPs are selected as shapelet candidates. Through these two steps, the number of shapelet candidates is sharply reduced so that the training time is also sharply reduced, which ensures efficient training and feature extraction in an online time series classification system. The experiments showed the JSSS can get results in less than 30 s in the worst condition for all the datasets. At the same time, classification accuracy rates improved by more than 9.9% in the offline scenario and 7.1% in the online scenario.

A fast shapelet selection algorithm for time series classification

Time series classification has attracted significant interest over the past decade. One of the promising approaches is shapelet based algorithms, which are interpretable, more accurate and faster than most classifiers. However, the training time of shapelet based algorithms is high, even though it is computed off-line. To overcome this problem, in this paper, we propose a fast shapelet selection algorithm (FSS), which sharply reduces the time consumption of shapelet selection. In our algorithm, we first sample some time series from a training dataset with the help of a subclass splitting method. Then FSS identifies the local farthest deviation points (LFDPs) for the sampled time series and selects the subsequences between two nonadjacent LFDPs as shapelet candidates. Using these two steps, the number of shapelet candidates is greatly reduced, which leads to an obvious reduction in time complexity. Unlike other methods that accelerate the shapelet selection process at the expense of a reduction in accuracy, the experimental results demonstrate that FSS is thousands of times faster than the original shapelet transformation method, with no reduction in accuracy. Our results also demonstrate that our method is the fastest among shapelet based methods that have the leading level of accuracy.

Association mapping in biomedical time series via statistically significant shapelet mining.

MotivationMost modern intensive care units record the physiological and vital signs of patients. These data can be used to extract signatures, commonly known as biomarkers, that help physicians understand the biological complexity of many syndromes. However, most biological biomarkers suffer from either poor predictive performance or weak explanatory power. Recent developments in time series classification focus on discovering shapelets, i.e. subsequences that are most predictive in terms of class membership. Shapelets have the advantage of combining a high predictive performance with an interpretable component—their shape. Currently, most shapelet discovery methods do not rely on statistical tests to verify the significance of individual shapelets. Therefore, identifying associations between the shapelets of physiological biomarkers and patients that exhibit certain phenotypes of interest enables the discovery and subsequent ranking of physiological signatures that are interpretable, statistically validated and accurate predictors of clinical endpoints.ResultsWe present a novel and scalable method for scanning time series and identifying discriminative patterns that are statistically significant. The significance of a shapelet is evaluated while considering the problem of multiple hypothesis testing and mitigating it by efficiently pruning untestable shapelet candidates with Tarone’s method. We demonstrate the utility of our method by discovering patterns in three of a patient’s vital signs: heart rate, respiratory rate and systolic blood pressure that are indicators of the severity of a future sepsis event, i.e. an inflammatory response to an infective agent that can lead to organ failure and death, if not treated in time.Availability and implementationWe make our method and the scripts that are required to reproduce the experiments publicly available at https://github.com/BorgwardtLab/S3M.Supplementary information Supplementary data are available at Bioinformatics online.

A Shapelet Selection Algorithm for Time Series Classification: New Directions

Time series classification (TSC) has attracted significant interest over the past decade. One of the promising recent approaches are shapelet based algorithms, which are interpretable, more accurate, and faster than most classifiers. However, the high time complexity of shapelet selection process hinders its application in real time data procession. To overcome this, in this paper we propose a fast shapelet selection algorithm (FSS), which sharply reduces the time consumption of shapelet selection. In our algorithm, we first sample some time series from training data set with the help of a subclass splitting method. Then FSS identifies the local farthest deviation points (LFDPs) for sampled time series and selects the subsequences between two nonadjacent LFDPs as shapelet candidates. Through these two steps, the number of shapelet candidates is greatly reduced, which leads to an obvious reduction in time complexity. Unlike other methods which accelerate shapelet selection process at the expense of reducing accuracy, the experimental results demonstrate that FSS is thousands of times faster than ST with no accuracy reduced. Our results also demonstrate that our methods is the fastest method among the shapelet-based methods which have the leading level of accuracy.

Discriminative extraction of features from time series

Abstract A primary challenge of time series classification is how to extract powerful features from training samples. Two kinds of classification methods, global-based and local-based methods, have been studied widely in recent years. The global-based methods, like 1-Nearest Neighbor(1-NN), take the entire series as features, which have the drawback that they are not able to indicate the intrinsic characters of a class. The local-based methods overcome this weakness by employing discriminative time series subsequences as features, called shapelets. However, most local-based methods are computationally expensive because of the massive number of shapelet candidates. In this paper, we propose a novel shapelets extraction method which takes each time series as a high-dimensional data and then finds the discriminative dimensions corresponding to the positions of shapelets. More specifically, the discriminative dimensions are determined by combining Local Fisher Discriminant Analysis (LFDA) method and two sparse restrictions which can encourage the continuous characteristic of time series. Extensive experimental results show that the proposed method achieves significant improvement compared to the existing shapelet-based methods in terms of classification accuracy and running time on the commonly used time series datasets. In addition, comparing with the accepted time series classification methods, NNDTW and COTE, our method still gets better results.

Un cas concret…

Time series classification (TSC) has attracted significant interest over the past decade. One of the promising recent approaches are shapelet based algorithms, which are interpretable, more accurate, and faster than most classifiers. However, the high time complexity of shapelet selection process hinders its application in real time data procession. To overcome this, in this paper we propose a fast shapelet selection algorithm (FSS), which sharply reduces the time consumption of shapelet selection. In our algorithm, we first sample some time series from training data set with the help of a subclass splitting method. Then FSS identifies the local farthest deviation points (LFDPs) for sampled time series and selects the subsequences between two nonadjacent LFDPs as shapelet candidates. Through these two steps, the number of shapelet candidates is greatly reduced, which leads to an obvious reduction in time complexity. Unlike other methods which accelerate shapelet selection process at the expense of reducing accuracy, the experimental results demonstrate that FSS is thousands of times faster than ST with no accuracy reduced. Our results also demonstrate that our methods is the fastest method among the shapelet-based methods which have the leading level of accuracy.