A fast shapelet selection algorithm for time series classification

Ji, Cun; Zhao, Chenxiao; Liu, Shijun; Yang, Chenglei; Pan, Li; Wu, Lei; Meng, Xiangxu

doi:10.1016/j.comnet.2018.11.031

Cited by 61 publications

(28 citation statements)

References 24 publications

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“…58 In the least squares method, k and b in the linear approximation function Y = kX + b are calculated using (8) and (9), respectively. In (8) and (9),X andȲ can be calculated using (7).…”

Section: Identify Abnormal Points or Change Points Based On The Bisermentioning

confidence: 99%

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ADARC: An anomaly detection algorithm based on relative outlier distance and biseries correlation

Zou

Liu

et al. 2019

Softw Pract Exp

Self Cite

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The application of anomaly detection to data monitoring is a fundamental requirement of the public service systems of a smart city. Many detection methods have been proposed for identifying anomalous situations, including methods based on periodicity or biseries correlations. However, the detection results of these methods are not ideal. Thus, we present a new anomaly detection algorithm for time series based on the relative outlier distance (ROD) and biseries correlations. The proposed algorithm detects outliers based on the ROD and identifies abnormal points and change points based on biseries correlations. Experimental results show that our method achieves better recall and F1-measure scores than various time series-based techniques while maintaining a high level of precision.

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“…58 In the least squares method, k and b in the linear approximation function Y = kX + b are calculated using (8) and (9), respectively. In (8) and (9),X andȲ can be calculated using (7).…”

Section: Identify Abnormal Points or Change Points Based On The Bisermentioning

confidence: 99%

“…A time series represents a collection of values obtained from sequential measurements over time. [7][8][9][10] Time series data are numerical, continuous, and continuously updated. [11][12][13] In a smart city, every node in the public service systems must be monitored.…”

Section: Introductionmentioning

confidence: 99%

ADARC: An anomaly detection algorithm based on relative outlier distance and biseries correlation

Zou

Liu

et al. 2019

Softw Pract Exp

Self Cite

View full text Add to dashboard Cite

show abstract

“…These methods can be roughly divided into four categories: (1) training instances are selected to generate the candidate shapelets. For example, Ji et al [ 29 ] put forward a subclass splitting method to sample the training instances for candidate shapelet generation. (2) Heuristic shapelet search method.…”

Section: Introductionmentioning

confidence: 99%

Shapelet Discovery by Lazy Time Series Classification

Zhang

Wang

Yuan

et al. 2020

Computational Intelligence and Neuroscience

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As a representation of discriminative features, the time series shapelet has recently received considerable research interest. However, most shapelet-based classification models evaluate the differential ability of the shapelet on the whole training dataset, neglecting characteristic information contained in each instance to be classified and the classwise feature frequency information. Hence, the computational complexity of feature extraction is high, and the interpretability is inadequate. To this end, the efficiency of shapelet discovery is improved through a lazy strategy fusing global and local similarities. In the prediction process, the strategy learns a specific evaluation dataset for each instance, and then the captured characteristics are directly used to progressively reduce the uncertainty of the predicted class label. Moreover, a shapelet coverage score is defined to calculate the discriminability of each time stamp for different classes. The experimental results show that the proposed method is competitive with the benchmark methods and provides insight into the discriminative features of each time series and each type in the data.

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“…All these services continue to drive the demand for higher data rates and lead to crowdsourcing application in Internet-of-ings (IoT), where pervasive interconnected smart objects cooperates together to reach multiple goals. IoT technologies can e ectively promote the interactions between environments and the human and enhance the reliability and e ciency of smart cities [2][3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Optimal Task Partition with Delay Requirement in Mobile Crowdsourcing

Zhai

Wang

2019

Wireless Communications and Mobile Computing

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Mobile crowdsourcing takes advantage of mobile devices such as smart phones and tablets to process data for a lot of applications (e.g., geotagging for mobile touring guiding monitoring and spectrum sensing). In this paper, we propose a mobile crowdsourcing paradigm to make a task requester exploit encountered mobile workers for high-quality results. Since a task may be too complex for a single worker, it is necessary for a task requester to divide a complex task into several parts so that a mobile worker can nish a part of the task easily. We describe the task crowdsourcing process and propose the worker arrival model and task model. Furthermore, the probability that all parts of the complicated task are executed by mobile workers is introduced to evaluate the result of task crowdsourcing. Based on these models, considering computing capacity and rewards for mobile workers, we formulate a task partition problem to maximize the introduced probability which is used to evaluate the result of task crowdsourcing. en, using a Markov chain, a task partition policy is designed for the task requester to realize high-quality mobile crowdsourcing. With this task partition policy, the task requester is able to divide the complicated task into precise number of parts based on mobile workers' arrival, and the probability that the total parts are executed by mobile workers is maximized. Also, the invalid number of task assignment attempts is analyzed accurately, which is helpful to evaluate the resource consumption of requesters due to probing potential workers. Simulations show that our task partition policy improves the results of task crowdsourcing.

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A fast shapelet selection algorithm for time series classification

Cited by 61 publications

References 24 publications

ADARC: An anomaly detection algorithm based on relative outlier distance and biseries correlation

ADARC: An anomaly detection algorithm based on relative outlier distance and biseries correlation

Shapelet Discovery by Lazy Time Series Classification

Optimal Task Partition with Delay Requirement in Mobile Crowdsourcing

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