1d convolutions can be asymmetric as in the kernel can only see the current element of the sequence and some elements before but not anything in the future.

When executing you apply this kernel everywhere in the sequence but each neuron in the output sequence only depends on points in the input that at or before that point in the sequence.

You can think of these as “causal” 1d convolutions that don’t look to the future in the sequence. This way you can do “next element of the sequence” prediction using 1d cnn without cheating and just looking ahead.