# Can CNNs be applied to non-image data, given that the convolution and pooling operations are mainly applied to imagery?

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)?