ORCID Profile
0000-0002-3763-5080
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Publisher: Springer Nature Singapore
Date: 2023
Publisher: IOP Publishing
Date: 11-2019
DOI: 10.1088/1757-899X/692/1/012047
Abstract: Home appliance test is an essential part of the R& D process. However, since home appliance test must be carried out under certain circumstances, it makes the development cycle longer. If it is possible to predicting the home appliance test data, it will improve the efficiency of home appliance development. Sequence to sequence model has been proved that it has a strong ability to map the input sequence and output sequence and it has been widely used in machine translation tasks. In order to solve the home appliance test data prediction problem, we firstly tried to apply sequence to sequence model to predict numerically continuous time series data. Our experiments proved that our model can predict the data of relatively long-time steps when inputting data of relatively short time steps. In our experiment, the mean absolute percentage error of the prediction of our model is about 0.1%. Our approach has a strong generalization ability and performs well in different experimental scenarios. Finally, we believe that the sequence to sequence method performs satisfactorily on prediction problems for continuous time series data. With this model, we can obtain more accurate results than traditional methods and it is meaningful for the R& D of home appliances.
Publisher: Frontiers Media SA
Date: 11-02-2020
Publisher: IEEE
Date: 07-2023
Publisher: Springer International Publishing
Date: 2022
Publisher: Elsevier BV
Date: 2024
Publisher: IEEE
Date: 07-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: ACM
Date: 22-05-2020
Publisher: IEEE
Date: 23-05-2022
Publisher: ACM
Date: 27-02-2023
Publisher: Springer Science and Business Media LLC
Date: 29-06-2023
DOI: 10.1007/S11063-022-10944-0
Abstract: Multivariate time series classification is a critical problem in data mining with broad applications. It requires harnessing the inter-relationship of multiple variables and various ranges of temporal dependencies to assign the correct classification label of the time series. Multivariate time series may come from a wide range of sources and be used in various scenarios, bringing the classifier challenge of temporal representation learning. We propose a novel convolutional neural network architecture called Attentional Gated Res2Net for multivariate time series classification. Our model uses hierarchical residual-like connections to achieve multi-scale receptive fields and capture multi-granular temporal information. The gating mechanism enables the model to consider the relations between the feature maps extracted by receptive fields of multiple sizes for information fusion. Further, we propose two types of attention modules, channel-wise attention and block-wise attention, to better leverage the multi-granular temporal patterns. Our experimental results on 14 benchmark multivariate time-series datasets show that our model outperforms several baselines and state-of-the-art methods by a large margin. Our model outperforms the SOTA by a large margin, the classification accuracy of our model is 10.16% better than the SOTA model. Besides, we demonstrate that our model improves the performance of existing models when used as a plugin. Further, based on our experiments and analysis, we provide practical advice on applying our model to a new problem.
No related grants have been discovered for Chao Yang.