Fast and Accurate Time Series Classification Through Supervised Interval Search

Cabello, Nestor; Naghizade, Elham; Qi, Jianzhong; Kulik, Lars

doi:10.1109/icdm50108.2020.00107

Cited by 37 publications

(15 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…TDE is significantly more accurate than WEASEL and S-BOSS, which in turn are more accurate than BOSS and cBOSS. (Cabello et al 2020) and the feature set method defined as the canonical time series characteristics (catch22) (Lubba et al 2019). The catch22 features are a set of 22 features designed for time series data filtered from the 7658 features available in the highly comparative time series analysis (hctsa) toolbox (Fulcher and Jones 2017).…”

Section: Temporal Dictionary Ensemble (Tde)mentioning

confidence: 99%

“…Proximity Forest (Lucas et al 2019) is a tree ensemble that randomly chooses distance functions at each node. Supervised Time Series Forest (STSF) (Cabello et al 2020) is an interval based tree ensemble that includes a supervised method for extracting intervals and uses summary statistics and spectral features. A number of extensions to the BOSS classifier have been made since the bake off in S-BOSS (Large et al 2019a), cBOSS (Middlehurst et al 2019) and WEASEL (Schäfer and Leser 2017a).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

HIVE-COTE 2.0: a new meta ensemble for time series classification

et al. 2021

View full text Add to dashboard Cite

The Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE) is a heterogeneous meta ensemble for time series classification. HIVE-COTE forms its ensemble from classifiers of multiple domains, including phase-independent shapelets, bag-of-words based dictionaries and phase-dependent intervals. Since it was first proposed in 2016, the algorithm has remained state of the art for accuracy on the UCR time series classification archive. Over time it has been incrementally updated, culminating in its current state, HIVE-COTE 1.0. During this time a number of algorithms have been proposed which match the accuracy of HIVE-COTE. We propose comprehensive changes to the HIVE-COTE algorithm which significantly improve its accuracy and usability, presenting this upgrade as HIVE-COTE 2.0. We introduce two novel classifiers, the Temporal Dictionary Ensemble and Diverse Representation Canonical Interval Forest, which replace existing ensemble members. Additionally, we introduce the Arsenal, an ensemble of ROCKET classifiers as a new HIVE-COTE 2.0 constituent. We demonstrate that HIVE-COTE 2.0 is significantly more accurate on average than the current state of the art on 112 univariate UCR archive datasets and 26 multivariate UEA archive datasets.

show abstract

Section: Temporal Dictionary Ensemble (Tde)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

HIVE-COTE 2.0: a new meta ensemble for time series classification

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Proximity Forest (Lucas et al, 2019) is a tree ensemble that randomly chooses distance functions at each node. Supervised Time Series Forest (STSF) (Cabello et al, 2020) is an interval based tree ensemble that includes a supervised method for extracting intervals and uses summary statistics and spectral features. A number of extensions to the BOSS classifier have been made since the bakeoff in S-BOSS (Large et al, 2019a), cBOSS and WEASEL (Schäfer and Leser, 2017a).…”

Section: Ts-chiefmentioning

confidence: 99%

HIVE-COTE 2.0: a new meta ensemble for time series classification

Middlehurst¹,

Large²,

Flynn³

et al. 2021

Preprint

View full text Add to dashboard Cite

The Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE) is a heterogeneous meta ensemble for time series classification. HIVE-COTE forms its ensemble from classifiers of multiple domains, including phase-independent shapelets, bag-of-words based dictionaries and phase-dependent intervals. Since it was first proposed in 2016, the algorithm has remained state of the art for accuracy on the UCR time series classification archive. Over time it has been incrementally updated, culminating in its current state, HIVE-COTE 1.0. During this time a number of algorithms have been proposed which match the accuracy of HIVE-COTE. We propose comprehensive changes to the HIVE-COTE algorithm which significantly improve its accuracy and usability, presenting this upgrade as HIVE-COTE 2.0. We introduce two novel classifiers, the Temporal Dictionary Ensemble (TDE) and Diverse Representation Canonical Interval Forest (DrCIF), which replace existing ensemble members. Additionally, we introduce the Arsenal, an ensemble of ROCKET classifiers as a new HIVE-COTE 2.0 constituent. We demonstrate that HIVE-COTE 2.0 is significantly more accurate than the current state of the art on 112 univariate UCR archive datasets and 26 multivariate UEA archive datasets.Keywords Time series classification • Multivariate time series • Heterogeneous ensembles • HIVE-COTE

show abstract

“…The results of our proposed model also outperformed most published results on this dataset. The Supervised Time Series Forest (STSF) algorithm developed by Cabello et al [58] obtained an accuracy of 99.03% (average of ten runs). STSF is a time series forest for classification and feature extraction based on some discriminatory intervals.…”

Section: Experiments 3: Publicly Available Datasetmentioning

confidence: 99%

End-to-End Control Chart Pattern Classification Using a 1D Convolutional Neural Network and Transfer Learning

Cheng

Chiu

2021

Processes

View full text Add to dashboard Cite

Control charts are an important tool in statistical process control (SPC). They have been commonly used for monitoring process variation in many industries. Recognition of non-random patterns is an important task in SPC. The presence of non-random patterns implies that a process is affected by certain assignable causes, and some corrective actions should be taken. In recent years, a great deal of research has been devoted to the application of machine learning (ML) based approaches to control chart pattern recognition (CCPR). However, there are some gaps that hinder the application of the CCPR methods in practice. In this study, we applied a control chart pattern recognition method based on an end-to-end one-dimensional convolutional neural network (1D CNN) model. We proposed some methods to generate datasets with high intra-class diversity aiming to create a robust classification model. To address the data scarcity issue, some data augmentation operations suitable for CCPR were proposed. This study also investigated the usefulness of transfer learning techniques for the CCPR task. The pre-trained model using normally distributed data was used as a starting point and fine-tuned on the unknown non-normal data. The performance of the proposed approach was evaluated by real-world data and simulation experiments. Experimental results indicate that our proposed method outperforms the traditional machine learning methods and could be a promising tool to effectively classify control chart patterns. The results and findings of this study are crucial for the further realization of smart statistical process control.

show abstract

Fast and Accurate Time Series Classification Through Supervised Interval Search

Cited by 37 publications

References 13 publications

HIVE-COTE 2.0: a new meta ensemble for time series classification

HIVE-COTE 2.0: a new meta ensemble for time series classification

HIVE-COTE 2.0: a new meta ensemble for time series classification

End-to-End Control Chart Pattern Classification Using a 1D Convolutional Neural Network and Transfer Learning

Contact Info

Product

Resources

About