When using CNNs for non-image (times series) data prediction, what are some constraints or things to look out for as compared to image data?
To be more precise, I notice there are different types of layers in a CNN model, as described below, which seem to be particularly designed for image data.
A convolutional layer that extracts features from a source image. Convolution helps with blurring, sharpening, edge detection, noise reduction, or other operations that can help the machine to learn specific characteristics of an image.
A pooling layer that reduces the image dimensionality without losing important features or patterns.
A fully connected layer also known as the dense layer, in which the results of the convolutional layers are fed through one or more neural layers to generate a prediction.
Are these operations also applicable to non-image data (for example, times series)?