I want to use CNN architectures for classification of multivariate time-series, where we apply one label to each sequence. I searched the net for the available designs in the literature and i found the following options. Assuming each MV time-series $\mathbf{S}$ is defined in $\mathcal{R}^{n \times t}$, where n is the number of dimensions and $t$ is the length of the sequence:
- Treating each $\mathbf{S}$ as a $n \times T$ image as the input of CNN.
- Use $n$ separate input channels of size $1 \times T$ and apply a CNN with 1D conv-filters for such input.
- Using $n$ separate CNNs each of which is associated with one dimension of $\mathbf{S}$ and combine their extracted features as the inputs of the fully connected layer.
So, I was wondering if there is any more efficient architecture suggested for such type of data
Also, as usually there an inconsistency between the sequence length of different time-series, is there any smart solution suggested for that?
