Is it reasonable to use a CNN instead of an LSTM, even though it is a time series?
Yes, it is. Convolutional Neural Networks are applied to any kind of data in which neighboring information is supposedly relevant for the analysis of the data.
CNN are very popular with images, where data is correlated in space, and in video, where correlation happens both in space and time.
Are there any indicators for when you should never switch to a CNN?
CNNs are limited in a sense: they have a rigid, forward structure.
If you are trying to perform:
- classification in sequences that have varying length ($N$ to $1$);
- trying to output another sequence which has no fixed proportion between their length and the length of the input ($N$ to $M$).
Simple feed-forward neural networks will fail (due to dimension inconsistency). So, in that case you should use a recursive neural network such as a LSTM.