Convolutional Neural Networks for Time Series Classification

By Mariusz Zebik, M. Korytkowski, R. Angryk, R. Scherer. International Conference on Artificial Intelligence and Soft Computing, 2017

440 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Signal Processing

View Paper: https://www.semanticscholar.org/paper/d3229d6cf3738b31ee23e815e2a8c799df5c82dd

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. Ming Jin
2. Geoffrey I. Webb
3. Yusuf Yaslan
4. Cesare Alippi
5. Shirui Pan
6. Zehra Cataltepe
7. Qingsong Wen
8. Hakan Gunduz

In Signal Processing

1. Geoffrey I. Webb
2. Yusuf Yaslan
3. Cesare Alippi
4. Shirui Pan
5. Zehra Cataltepe
6. Qingsong Wen
7. Hakan Gunduz
8. Weiwei Jiang

Citations in the corpus

• A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection (2023, background): “The advent of deep learning technologies has led to the development of different neural networks based on convolutional neural networks (CNN) [24], [25], recurrent neural networks (RNN) [26], and transformers [27], which have shown significant advantages in modeling real-world time series data.”
• Intraday prediction of Borsa Istanbul using convolutional neural networks and feature correlations (2017, methodology): “AC CE PT ED M AN US CR IP T Recently, Deep Neural Networks (DNN) and in particular Convolutional Neural Networks (CNN) have been applied to time series data [39, 40] due to their strong generalization and noise tolerance performance [12].”