MICOS: Mixed supervised contrastive learning for multivariate time series classification

Hao, Shilei; Wang, Zhihai; Alexander, Afanasiev D.; Yuan, Jiazheng; Zhang, Wei

doi:10.1016/j.knosys.2022.110158

Cited by 19 publications

(3 citation statements)

References 21 publications

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“…CL for MTS Data Pioneering works have successfully utilized CL techniques to learn decent representations from unlabeled MTS data, primarily focusing on achieving temporal consistency (Pöppelbaum, Chadha, and Schwung 2022;Khaertdinov, Ghaleb, and Asteriadis 2021;Hao et al 2023;Yue et al 2022;Eldele et al 2021). Specifically, they augmented MTS data with temporal augmentations such as jittering, cropping, and sub-series, and then conducted CL to ensure encoders robustness to temporal disturbances.…”

Section: Related Workmentioning

confidence: 99%

Graph-Aware Contrasting for Multivariate Time-Series Classification

Wang,

Xu,

Yang

et al. 2024

AAAI

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Contrastive learning, as a self-supervised learning paradigm, becomes popular for Multivariate Time-Series (MTS) classification. It ensures the consistency across different views of unlabeled samples and then learns effective representations for these samples. Existing contrastive learning methods mainly focus on achieving temporal consistency with temporal augmentation and contrasting techniques, aiming to preserve temporal patterns against perturbations for MTS data. However, they overlook spatial consistency that requires the stability of individual sensors and their correlations. As MTS data typically originate from multiple sensors, ensuring spatial consistency becomes essential for the overall performance of contrastive learning on MTS data. Thus, we propose Graph-Aware Contrasting for spatial consistency across MTS data. Specifically, we propose graph augmentations including node and edge augmentations to preserve the stability of sensors and their correlations, followed by graph contrasting with both node- and graph-level contrasting to extract robust sensor- and global-level features. We further introduce multi-window temporal contrasting to ensure temporal consistency in the data for each sensor. Extensive experiments demonstrate that our proposed method achieves state-of-the-art performance on various MTS classification tasks. The code is available at https://github.com/Frank-Wang-oss/TS-GAC.

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Section: Related Workmentioning

confidence: 99%

Graph-Aware Contrasting for Multivariate Time-Series Classification

Wang,

Xu,

Yang

et al. 2024

AAAI

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“…Each approach can be applied according to the research domain being worked on. The supervised learning approach is known as the machine learning development process, which requires labeled data [5][6][7], while unsupervised learning is without labeled data [8][9][10]. Supervised learning is applied to develop classification models and requires labeled data.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore it is necessary to apply the confusion matrix, Cohen Kappa, and Matthews correlation coefficient (MCC). A total of 40 datasets with multiple ordinal from various fields, including social sciences (16), life sciences (13), engineering (4), and other fields (7). Based on the results of classification performance measurements, it is known that MCC is able to outperform the confusion matrix and Cohen kappa [23].…”

Section: Introductionmentioning

confidence: 99%

Labeling Consistency Test of Multi-Label Data for Aspect and Sentiment Classification Using the Cohen Kappa Method

Chamid,

Widowati,

Kusumaningrum

2024

ISI

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Classification is part of machine learning, and developing it requires labeled data. Most data is available in an unlabeled form. Data labeling is a step that researchers must take. Good labeled data will produce a good classification model. The data labeling process cannot be ignored and needs to be done carefully and consistently. Because the classification process requires well-labeled data that can be accounted for. In addition, good labeled data will produce a good classification model. The role of an expert (rater) is needed to label the data and ideally at least two experts. However, involving two raters will become a new problem because it is likely that the results of the inter-rater labeling will be different. We propose the Cohen Kappa method to overcome this problem. We used data from scraping user reviews of the Indonesian marketplace, there were 4.307. Based on the calculation results, Kappa=0.909 for aspect detection, Kappa=0.893 for sentiment classification, and Kappa=0.971 for class aspect. Based on the kappa value, the labeling results for aspect detection, sentiment classification and aspect class were declared "almost perfect agreement", so that the results of this research obtained labeled data that can be used for classification tasks, especially for developing aspect-based sentiment analysis models.

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Multiscale spatial‐temporal transformer with consistency representation learning for multivariate time series classification

Wu,

Qiu,

Wang

et al. 2024

Concurrency and Computation

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SummaryMultivariate time series classification holds significant importance in fields such as healthcare, energy management, and industrial manufacturing. Existing research focuses on capturing temporal changes or calculating time similarities to accomplish classification tasks. However, as the state of the system changes, capturing spatial‐temporal consistency within multivariate time series is key to the ability of the model to classify accurately. This paper proposes the MSTformer model, specifically designed for multivariate time series classification tasks. Based on the Transformer architecture, this model uniquely focuses on multiscale information across both time and feature dimensions. The encoder, through a designed learnable multiscale attention mechanism, divides data into sequences of varying temporal scales to learn multiscale temporal features. The decoder, which receives the spatial view of the data, utilizes a dynamic scale attention mechanism to learn spatial‐temporal consistency in a one‐dimensional space. In addition, this paper proposes an adaptive aggregation mechanism to synchronize and combine the outputs of the encoder and decoder. It also introduces a multiscale 2D separable convolution designed to learn spatial‐temporal consistency in two‐dimensional space, enhancing the ability of the model to learn spatial‐temporal consistency representation. Extensive experiments were conducted on 30 datasets, where the MSTformer outperformed other models with an average accuracy rate of 85.6%. Ablation studies further demonstrate the reliability and stability of MSTformer.

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MICOS: Mixed supervised contrastive learning for multivariate time series classification

Cited by 19 publications

References 21 publications

Graph-Aware Contrasting for Multivariate Time-Series Classification

Graph-Aware Contrasting for Multivariate Time-Series Classification

Labeling Consistency Test of Multi-Label Data for Aspect and Sentiment Classification Using the Cohen Kappa Method

Multiscale spatial‐temporal transformer with consistency representation learning for multivariate time series classification

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