Time series analysis via Residual Neural Networks

Time series analysis using Residual neural networks. The main task is to explore how different ResNets can be combined to solve complex time series problems.

Usually, Recurrent neural networks are used for time series analysis. Nevertheless, there exists also work for time series analysis that utilizes convolutional neural networks (CNN). ResNets being an advanced CNN is the natural next step to improve time series classification and prediction.


The goal is to research and develop new deep neural network architectures that utilize ResNets for the task. The main task is to explore and evaluate the capabilities of ResNet based time series analysis for classification or prediction. The architectures should be evaluated and verified using different real-world datasets (we have datasets from different domains from sport to medicine that can be used).

Learning outcome

  • Deep understanding of CNNs and ResNets
  • Working on a real-world application
  • Collaboration with researchers
  • Possibility to implement and research a novel approach


  • Python programming
  • Knowledge about deep learning is an advantage


  • Pål Halvorsen
  • Michael Riegler
  • Steven Hicks


He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. InProceedings of the IEEE conference on computer vision and pattern recognition 2016 (pp. 770-778).

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