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[D] RNN/CNN architectures for time series and considering the stochastic process modelling the residuals

For a small project I have been looking at applying primarily RNN architectures to a multivariate time series problem. However, research that provides reasonable assumptions on how to model the time series in terms of the residual and a neural network seems to be hard to find. Although for long term simulation of scenarios this seems to be a core aspect of analyzing time series, considering classical time series research.

I would be very grateful for pointing out good papers concerning neutral nets in time series or would like to talk about what kind of stochastic model people having experience chose and why. As a starting point I suggest RNN architectures can fit the trend, seasonality and an ARMA(p,q) model. However modelling time dependent variance and extremal values seems to be a rather hard task for neural nets.

Sorry for not having great academic english and looking forward to your answers!

submitted by /u/joergengogogo
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.