Process Monitoring and Fault Prediction in Multivariate Time Series Using Bag-of-Words

Guo, Shenghan; Guo, Weihong

doi:10.1109/tase.2020.3026065

Cited by 13 publications

(1 citation statement)

References 63 publications

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“…Several variants of DTW are compared in [2]. At the same time, some time series discretization techniques are proposed, such as symbolic aggregate approximation (SAX) [3], to reduce time series dimension and improve computational efficiency. Fuzzy similarity (FS) [4] is adapted into the characterizing defects problem, which is capable of processing time series signals affected by uncertainty and inaccuracy.…”

Section: Introductionmentioning

confidence: 99%

Time Series Classification by Shapelet Dictionary Learning with SVM‐Based Ensemble Classifier

Zhang

Shen

Gao

et al. 2021

Computational Intelligence and Neuroscience

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Time series classification is a basic and important approach for time series data mining. Nowadays, more researchers pay attention to the shape similarity method including Shapelet-based algorithms because it can extract discriminative subsequences from time series. However, most Shapelet-based algorithms discover Shapelets by searching candidate subsequences in training datasets, which brings two drawbacks: high computational burden and poor generalization ability. To overcome these drawbacks, this paper proposes a novel algorithm named Shapelet Dictionary Learning with SVM-based Ensemble Classifier (SDL-SEC). SDL-SEC modifies the Shapelet algorithm from two aspects: Shapelet discovery method and classifier. Firstly, a Shapelet Dictionary Learning (SDL) is proposed as a novel Shapelet discovery method to generate Shapelets instead of searching them. In this way, SDL owns the advantages of lower computational cost and higher generalization ability. Then, an SVM-based Ensemble Classifier (SEC) is developed as a novel ensemble classifier and adapted to the SDL algorithm. Different from the classic SVM that needs precise parameters tuning and appropriate features selection, SEC can avoid overfitting caused by a large number of features and parameters. Compared with the baselines on 45 datasets, the proposed SDL-SEC algorithm achieves a competitive classification accuracy with lower computational cost.

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Section: Introductionmentioning

confidence: 99%