SpADe: On Shape-based Pattern Detection in Streaming Time Series

Chen, Yueguo; Nascimento, Mário A.; Ooi, Beng Chin; Tung, Anthony K. H.

doi:10.1109/icde.2007.367924

Cited by 128 publications

(93 citation statements)

References 11 publications

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“…More specifically, we attempt to cluster the similar-shape trajectories so as to be able to represent the user's movement behavior. In existing work, a shape-based pattern detection method has been used to detect streaming time series data [3]. We adopt the concept on trajectory data to find out the similar-shape trajectory.…”

Section: Shape-clusteringmentioning

confidence: 99%

Mining Top-K Relevant Stay Regions from Historical Trajectories

Lin

Lai

Lei

2014

Lecture Notes in Computer Science

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Abstract. With increasingly prevalent mobile positioning devices, such as GPS loggers, smart phones, and GPS navigation devices, a huge amount of trajectories data is collected. Users are able to obtain the various location-based services by uploading their trajectories. In this paper, we address that a user's movement behavior is able to discover by their similar shape trajectories and resulted in some regions frequently stay in common, called relevant stay regions. Once a set of stay regions discovered, we can predict the next region where the user intends to go and provide location-based information of the next stay in advance, such as traffic status, targeted advertises, sightseeing recommendations, and so on. Prior works have elaborated on discovering stay region from the whole crowd trajectories and then exploring the relations between the regions to describe the movement patterns for location prediction. However, the trajectories pass the same region may not have the similar movement behavior. Thus, we propose a framework to discover stay regions relevant to the specific movement behavior and then applied in location prediction, called Region Modeling and Mobility Prediction. The proposed framework includes two modules: region modeling and mobility prediction. In the region modeling module, we develop shape clustering method to group the similar trajectories from historical data and then explore the stay region model from trajectory clusters. Based on the discovered region model, the mobility prediction module provide a cluster selection algorithm and several prediction strategies to generate the topk relevant stay regions. Experiments results on real datasets demonstrate the effectiveness and accuracy of our proposed model on detecting next stay region, comparing with other baseline methods.

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Section: Shape-clusteringmentioning

confidence: 99%

Mining Top-K Relevant Stay Regions from Historical Trajectories

Lin

Lai

Lei

2014

Lecture Notes in Computer Science

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“…Many clustering methods have been proposed for time series data [8] [9][10] [7]. Among them, TSKmeans [7] is the most recently published.…”

Section: Related Workmentioning

confidence: 99%

A Weight-Based Clustering Method

2017

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This paper proposes a weight-based self-constructing clustering method for time series data. Selfconstructing clustering processes all the data points incrementally. If a data point is not similar enough to an existing cluster, then (1) if the point currently does not belong to any cluster, it forms a new cluster of its own; (2) otherwise, the point is removed from the cluster it currently belongs to before a new cluster is formed. However, if a data point is similar enough to an existing cluster, then (1) if the point currently does not belong to any cluster, it is added to the most similar cluster; (2) otherwise, it is removed from the cluster it currently belongs to and added to the most similar cluster. During the clustering process, weights are learned and considered in the calculations of similarity between data points and clusters. Experimental results show that our proposed approach performs more effectively than other methods for real world time series datasets.

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“…The third step is instance model construction, which introduces a trust region to model the time series segments corresponding to the selected landmark subsequence. Unlike most of the representation and similarity methods, which are designed mainly for full sequence matching [15], our proposed approach is capable of processing both full sequence and subsequence matching of various length, while being less sensitive to noise, and being able to handle deformations in both magnitude and temporal dimensions.…”

Section: Introductionmentioning

confidence: 99%

“…This paper focuses on the problem of detecting instances of predefined patterns from time series [15,56]. While most pattern detection algorithms in time series deal with discovering previously unknown, frequently recurring regularities in the data, here we assume that one or more example sequences (the templates) are provided by a domain expert, and instances of these need to be identified in the actual data.…”

Section: Introductionmentioning

confidence: 99%

Predefined pattern detection in large time series

Miao

Vespier

Cachucho

et al. 2016

Information Sciences

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a b s t r a c tPredefined pattern detection from time series is an interesting and challenging task. In order to reduce its computational cost and increase effectiveness, a number of time series representation methods and similarity measures have been proposed. Most of the existing methods focus on full sequence matching, that is, sequences with clearly defined beginnings and endings, where all data points contribute to the match. These methods, however, do not account for temporal and magnitude deformations in the data and result to be ineffective on several real-world scenarios where noise and external phenomena introduce diversity in the class of patterns to be matched. In this paper, we present a novel pattern detection method, which is based on the notions of templates, landmarks, constraints and trust regions. We employ the Minimum Description Length (MDL) principle for time series preprocessing step, which helps to preserve all the prominent features and prevents the template from overfitting. Templates are provided by common users or domain experts, and represent interesting patterns we want to detect from time series. Instead of utilising templates to match all the potential subsequences in the time series, we translate the time series and templates into landmark sequences, and detect patterns from landmark sequence of the time series. Through defining constraints within the template landmark sequence, we effectively extract all the landmark subsequences from the time series landmark sequence, and obtain a number of landmark segments (time series subsequences or instances). We model each landmark segment through scaling the template in both temporal and magnitude dimensions. To suppress the influence of noise, we introduce the concept of trust region, which not only helps to achieve an improved instance model, but also helps to catch the accurate boundaries of instances of the given template. Based on the similarities derived from instance models, we introduce the probability density function to calculate a similarity threshold. The threshold can be used to judge if a landmark segment is a true instance of the given template or not. To evaluate the effectiveness and efficiency of the proposed method, we apply it to two real-world datasets. The results show that our method is capable of detecting patterns of temporal and magnitude deformations with competitive performance.

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SpADe: On Shape-based Pattern Detection in Streaming Time Series

Cited by 128 publications

References 11 publications

Mining Top-K Relevant Stay Regions from Historical Trajectories

Mining Top-K Relevant Stay Regions from Historical Trajectories

A Weight-Based Clustering Method

Predefined pattern detection in large time series

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