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time-series-11

Semi-supervised time series classification

3260 papers • 126 benchmarks • 313 datasets

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Bosch CNC Machining Dataset

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Most implemented papers

Self-Supervised Contrastive Representation Learning for Semi-Supervised Time-Series Classification

Zhenghua Chen, Emadeldeen Eldele, Mohamed Ragab, Min Wu, C. Kwoh, Xiaoli Li, Cuntai Guan•Fri Aug 12 2022

This work proposes a novel Time-Series representation learning framework via Temporal and Contextual Contrasting (TS-TCC) that learns representations from unlabeled data with contrastive learning and proposes time-series-specific weak and strong augmentations to learn robust temporal relations in the proposed temporal contrasting module.

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127

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An Empirical Study of Graph-Based Approaches for Semi-supervised Time Series Classification

Dominik Alfke, Miriam Gondos, Lucile Peroche, M. Stoll•Thu Apr 15 2021

This study describes four different distance measures, including (Soft) DTW and MPDist, a distance measure based on the Matrix Profile, as well as four successful semi-supervised learning methods, including the recently introduced graph Allen–Cahn method and Graph Convolutional Neural Network method.

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Semi-Supervised Time Series Classification by Temporal Relation Prediction

Haoyi Fan, Fengbin Zhang, Zuoyong Li, Ruidong Wang, Xunhua Huang•Sat Jun 05 2021

Semi-supervised learning (SSL) has proven to be a powerful algorithm in different domains by leveraging unlabeled data to mitigate the reliance on the tremendous annotated data. However, few efforts consider the underlying temporal relation structure of unlabeled time series data in the semi-supervised learning paradigm. In this work, we propose a simple and effective method of Semi-supervised Time series classification architecture (termed as SemiTime) by gaining from the structure of unlabeled data in a self-supervised manner. Specifically, for the labeled time series, SemiTime conducts the supervised classification directly under the supervision of the annotated class label. For the unlabeled time series, the segments of past-future pair are sampled from time series, where two segments of pair from the same time series candidate are in positive temporal relation, while two segments from the different candidates are in negative temporal relation. Then, the temporal relation between those segments is predicted by SemiTime in a self-supervised manner. Finally, by jointly classifying labeled data and predicting the temporal relation of unlabeled data, the useful representation of unlabeled time series can be captured by SemiTime. Extensive experiments on multiple real-world datasets show that SemiTime consistently out-performs the state-of-the-arts, which demonstrates the effectiveness of the proposed method. Code and data are publicly available at https://haoyfan.github.io.

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SemiPFL: Personalized Semi-Supervised Federated Learning Framework for Edge Intelligence

Arvin Tashakori, Wenwen Zhang, Z. Jane Wang, P. Servati•Mon Mar 14 2022

It is demonstrated that SemiPFL outperforms state-of-the-art federated learning frameworks under the same assumptions regarding user performance, network footprint, and computational consumption, and the solution performs well for users without label or having limited labeled data sets and increasing performance for increased labeled data and number of users.

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Semi-supervised time series classification | State-of-the-Art