Exploiting multi-channels deep convolutional neural networks for multivariate time series classification

By Yi Zheng, Qi Liu, Enhong Chen, Yong Ge, J. Zhao. 2015

267 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Cornelis W. Oosterlee
2. Sander M. Bohte
3. Anastasia Borovykh

In Signal Processing

1. Cornelis W. Oosterlee
2. Sander M. Bohte
3. Anastasia Borovykh
4. Xiang Gao
5. Rajat Sen
6. Bryan Lim
7. Stefan Zohren

Citations in the corpus

• Conditional Time Series Forecasting with Convolutional Neural Networks (2017, methodology): “[30] for classification, using multiple conditional time series as inputs can improve both the robustness and forecast quality of the model by learning long-term temporal dependencies in between series.”
• Deep Learning for Sensor-based Activity Recognition: A Survey (2017, unknown): “For multi-modal signals, it is better to use CNN since (Bhattacharya and Lane, 2016) Body-worn, ambiemt RBM Gesture, ADL, transportation Self, D01 (Chen and Xue, 2015) Body-worn CNN ADL Self (Chen et al., 2016b) Body-worn CNN ADL D06 (Edel and K¨oppe, 2016) Body-worn RNN ADL D01, D04, Self (Fang and”
• Dilated convolutional neural networks for time series forecasting (2018, methodology): “As shown by e.g. [30] for classification, using multiple conditional time series as inputs can improve both the robustness and forecast quality of the model by learning long-term temporal dependencies in between series.”