  Multivariate time-series modeling and forecasting is an important problem
with numerous applications. Traditional approaches such as VAR (vector
auto-regressive) models and more recent approaches such as RNNs (recurrent
neural networks) are indispensable tools in modeling time-series data. In many
multivariate time series modeling problems, there is usually a significant
linear dependency component, for which VARs are suitable, and a nonlinear
component, for which RNNs are suitable. Modeling such times series with only
VAR or only RNNs can lead to poor predictive performance or complex models with
large training times. In this work, we propose a hybrid model called R2N2
(Residual RNN), which first models the time series with a simple linear model
(like VAR) and then models its residual errors using RNNs. R2N2s can be trained
using existing algorithms for VARs and RNNs. Through an extensive empirical
evaluation on two real world datasets (aviation and climate domains), we show
that R2N2 is competitive, usually better than VAR or RNN, used alone. We also
show that R2N2 is faster to train as compared to an RNN, while requiring less
number of hidden units.

Machine Learning;Machine Learning