  End-to-end data-driven machine learning methods often have exuberant
requirements in terms of quality and quantity of training data which are often
impractical to fulfill in real-world applications. This is specifically true in
time series domain where problems like disaster prediction, anomaly detection,
and demand prediction often do not have a large amount of historical data.
Moreover, relying purely on past examples for training can be sub-optimal since
in doing so we ignore one very important domain i.e knowledge, which has its
own distinct advantages. In this paper, we propose a novel knowledge fusion
architecture, Knowledge Enhanced Neural Network (KENN), for time series
forecasting that specifically aims towards combining strengths of both
knowledge and data domains while mitigating their individual weaknesses. We
show that KENN not only reduces data dependency of the overall framework but
also improves performance by producing predictions that are better than the
ones produced by purely knowledge and data driven domains. We also compare KENN
with state-of-the-art forecasting methods and show that predictions produced by
KENN are significantly better even when trained on only 50\% of the data.
