1d time series to time series approximation using deep learning
Yes, you can use one-dimensional convolutions but you have to consider the fact that the size of your inputs should be the same. In Tensorflow you can exploit tf.nn.conv1d for your purpose. RNN networks like LSTMs and GRUs are also other architectures that can be used. If your input has a patter that may happen different times in a single signal try to use conv1d. If you want to use RNNs you have to find which task you have such as a one-to-one or one-to-many-relation. For convolutional architecture, take a look at here and here, and take a look at [here] for RNNs.