Image vs Non-Image Data in CNN

When using CNN on 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 shown below, which seem to be particularly designed for image data, are they applicable to Non-Image Data(times series)?

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.

• One thing I can tell, is that well on translation invariant data like images, whereas time series data is not translation invariant, so try to transform it to translation invariant. – DuttaA Nov 29 '19 at 2:41
• @DuttaA Thanks for the advice. Could you please recommend some methods as to how I could transform time series data to translation invariant? – nilsinelabore Nov 29 '19 at 2:44