  Time series modeling techniques based on deep learning have seen many
advancements in recent years, especially in data-abundant settings and with the
central aim of learning global models that can extract patterns across multiple
time series. While the crucial importance of appropriate data pre-processing
and scaling has often been noted in prior work, most studies focus on improving
model architectures. In this paper we empirically investigate the effect of
data input and output transformations on the predictive performance of several
neural forecasting architectures. In particular, we investigate the
effectiveness of several forms of data binning, i.e. converting real-valued
time series into categorical ones, when combined with feed-forward, recurrent
neural networks, and convolution-based sequence models. In many non-forecasting
applications where these models have been very successful, the model inputs and
outputs are categorical (e.g. words from a fixed vocabulary in natural language
processing applications or quantized pixel color intensities in computer
vision). For forecasting applications, where the time series are typically
real-valued, various ad-hoc data transformations have been proposed, but have
not been systematically compared. To remedy this, we evaluate the forecasting
accuracy of instances of the aforementioned model classes when combined with
different types of data scaling and binning. We find that binning almost always
improves performance (compared to using normalized real-valued inputs), but
that the particular type of binning chosen is of lesser importance.
