<p>Do you know about the paper "<a href="https://arxiv.org/pdf/1611.06455" rel="noreferrer">Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline</a>"? If not, you should check it out. The authors provide a very comprehensive overview of different models, including a ResNet implementation adjusted for time series classification.</p>

<p>Their Keras/Tensorflow implementation of ResNet can be found <a href="https://github.com/cauchyturing/UCR_Time_Series_Classification_Deep_Learning_Baseline/blob/master/ResNet.py" rel="noreferrer">here</a>.</p>

<p><strong>Update:</strong> A more recent version of ResNet (and other classifiers) for time series data can be found <a href="https://github.com/hfawaz/dl-4-tsc" rel="noreferrer">here</a>.</p>
How to adapt ResNet to time series data
<p>I am trying to use the <a href="https://en.wikipedia.org/wiki/Residual_neural_network" rel="noreferrer">convolutional residual network neural network architecture</a> (ResNet). So far, I have implemented simple convolutions (conv1D) for time series data classification using Keras.</p>

<p>Now, I am trying to build ResNet using Keras but I'm having some difficulties trying to adapt it to time series data. Most of the implementations of ResNet or Nasnet in Keras (such as <a href="https://github.com/fchollet/deep-learning-models/blob/master/resnet50.py" rel="noreferrer">this one</a> or <a href="https://github.com/titu1994/Keras-NASNet/blob/master/nasnet.py" rel="noreferrer">that one</a>) use conv2D for their implementation (which makes sense for images).</p>

<p>Could someone help me in implementing this for time series data?</p>