Convolutional neural networks (CNN) were developed and remained very popular in the image classification domain. However, they can also be applied to 1-dimensional problems, such as predicting the next value in the sequence, be it a time series or the next word in a sentence.
In the following diagram, we present a simplified schema of a 1D CNN:
Based on the preceding diagram, we briefly describe the elements of a typical CNN architecture:
- Convolutional layer: The goal of this layer is to apply convolutional filtering to extract potential features.
- Pooling layer: This layer reduces the size of the image or series while preserving the important characteristics identified by the convolutional layer.
- Fully connected layer: Usually, there are a few fully connected layers at the end of the network to map the features extracted...
