Question

What is the best Neural Network Model for Temporal Data

Answer 1

I'm looking for a way to model and extract features from multivariate temporal data (e.g., multi-channel audio recordings).

I'm specifically interested in deep learning methods such as RBM, sparse autoencoders and so on.

Most methods I encountered consider only one dimension of the data, or maybe a 2D "block" in the data (usually images).

I couldn't find a paper on how to take into consideration the temporal aspect and also the multivariate aspect. For example if I'm recording audio from multiple channels, then I know that the channels are probably correlated to some degree. Also, since it's an evolving temporal signal, there is also some correlation of each sample to the previous samples.

Is there some way to incorporate these multivariate temporal constraints into a network?

I'm looking for specific papers on methods for multivariate temporal where the multi-channel structure is used.

Answer 2

"The brief and direct answer for this question would be the best Network model for Temporal Data is Recurrent Neural Network (RNN).

Now, here's a bit more descriptive version of the given statement. But first, to understand this, you need to know what a temporal data is and what a Recurrent Neural Network is.

So, temporal data is a special type of data which lacks consistency and varies over time. Recurrent Neural Network specializes in dealing with such type of data which varies over time or in other words, its best suited for Temporal Data."