A Co-training Approach for Noisy Time Series Learning

Author:

Zhang Weiqi1ORCID,Zhang Jianfeng2ORCID,Li Jia3ORCID,Tsung Fugee3ORCID

Affiliation:

1. The Hong Kong University of Science and Technology, Hong Kong SAR, Hong Kong

2. Huawei Noah's Ark Lab, Shenzhen, China

3. The Hong Kong University of Science and Technology (Guangzhou) & Hong Kong University of Science and Technology, Guangzhou & Hong Kong SAR, China

Funder

Foshan HKUST Projects

NSFC Grant

Guangzhou-HKUST(GZ) Joint Funding Scheme

Publisher

ACM

Reference58 articles.

1. S Abilasha , Sahely Bhadra , Ahmed Zaheer Dadarkar , and P Deepak . 2022 . Deep Extreme Mixture Model for Time Series Forecasting. In 31st ACM International Conference on Information and Knowledge Management: CIKM 2022. S Abilasha, Sahely Bhadra, Ahmed Zaheer Dadarkar, and P Deepak. 2022. Deep Extreme Mixture Model for Time Series Forecasting. In 31st ACM International Conference on Information and Knowledge Management: CIKM 2022.

2. Davide Anguita , Alessandro Ghio , Luca Oneto , Xavier Parra Perez , and Jorge Luis Reyes Ortiz . 2013 . A public domain dataset for human activity recognition using smartphones . In Proceedings of the 21th international European symposium on artificial neural networks, computational intelligence and machine learning. 437--442 . Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra Perez, and Jorge Luis Reyes Ortiz. 2013. A public domain dataset for human activity recognition using smartphones. In Proceedings of the 21th international European symposium on artificial neural networks, computational intelligence and machine learning. 437--442.

3. An iterative wavelet threshold for signal denoising

4. Combining labeled and unlabeled data with co-training

5. Application of machine learning techniques for supply chain demand forecasting

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