Journal of Systems Engineering and Electronics· 2017
Convolutional neural networks for time series classification
- 876citations
- 2017year
Short summary
A novel CNN framework automatically extracts deep features from time series, outperforming existing methods in classification accuracy and noise tolerance across simulated and real-world datasets.
AI-generated from the title and abstract; the full text is not read.
Key points
- A novel CNN framework is introduced for time series classification.
- CNNs automatically extract deep features via convolution and pooling, unlike traditional feature-based methods.
- The proposed method was tested on simulated and eight real-world datasets.
- Experimental results show the CNN outperforms state-of-the-art methods in accuracy and noise tolerance.
AI-generated from the title and abstract; the full text is not read.
Abstract
Time series classification is an important task in time series data mining, and has attracted great interests and tremendous efforts during last decades. However, it remains a challenging problem due to the nature of time series data: high dimensionality, large in data size and updating continuously. The deep learning techniques are explored to improve the performance of traditional feature-based approaches. Specifically, a novel convolutional neural network (CNN) framework is proposed for time series classification. Different from other feature-based classification approaches, CNN can discover and extract the suitable internal structure to generate deep features of the input time series automatically by using convolution and pooling operations. Two groups of experiments are conducted on simulated data sets and eight groups of experiments are conducted on real-world data sets from different application domains. The final experimental results show that the proposed method outperforms state-of-the-art methods for time series classification in terms of the classification accuracy and noise tolerance.
The authors' abstract, as published at the source. Journal of Systems Engineering and Electronics, 2017 · DOI ↗
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Field: Signal Processing
Signal ProcessingComputer Science