Recurrent Neural Networks (RNNs) have
become competitive forecasting methods, as most notably
shown in the winning method of the recent M4
competition. However, established statistical models such
as exponential smoothing (ETS) and the autoregressive
integrated moving average (ARIMA) gain their popularity
not only from their high accuracy, but also because they
are suitable for non-expert users in that they are robust,
efficient, and automatic. In these areas, RNNs have still a
long way to go. We present an extensive empirical study
and an open-source software framework of existing RNN
architectures for forecasting, and we develop guidelines
and best practices for their use. Recurrent neural
networks have been effectively used to predict outcomes
from irregular time series data in a variety of industries,
including medicine, traffic monitoring, environmental
monitoring, and human activity detection. The paper
focuses on two widely used methods for dealing with
irregular time series data: missing value imputation
during the data pre-processing stage and algorithm
modification to deal with missing values directly during
the learning process. Models that can handle problems
with irregular time series data are the only ones that are
reviewed; a wider variety of models that deal more widely
with sequences and regular time series are not included.
Keywords :
Time Series Forecasting, Recurrent Neural Networks, Deep-Latent Variable Models, Sensitivity Analysis and Time Series Data Prediction.